Revolutionizing Engineering Science 

through Simulation 

May 2006 

Report of the National Science Foundation 

Blue Ribbon Panel on 

Simulation-Based Engineering Science 


 ii
 iii

A Report of the National Science Foundation

Blue Ribbon Panel on

Simulation-Based Engineering Science

Revolutionizing Engineering Science

through Simulation

May 2006 
 iv
 v
 vi
 vii

THE NSF BLUE RIBBON PANEL ON

SIMULATION-BASED ENGINEERING SCIENCE

J. Tinsley Oden, Chair 

Associate Vice President for Research 

Director, Institute for Computational Engineering and Sciences 

Cockrell Family Regents Chair in Engineering 

The University of Texas at Austin 

Ted Belytschko 

Walter P. Murphy Professor and McCormick Professor 

Department of Mechanical Engineering 

Northwestern University 

Jacob Fish 

The Rosalind and John J. Redfern, Jr.’33 Chaired Professor in Engineering 

Department of Mechanical, Aerospace and Nuclear Engineering 

Rensselaer Polytechnic Institute 

Thomas J.R. Hughes 

Professor, Aerospace Engineering and Engineering Mechanics 

Computational and Applied Mathematics Chair III 

Institute for Computational Engineering and Sciences 

The University of Texas at Austin 

Chris Johnson 

Director, Scientific Computing and Imaging Institute 

Co-Director, Center for Integrated Biomedical Computing 

Distinguished Professor, Computer Science 

Research Professor, Bioengineering 

Adjunct Professor, Physics 

University of Utah 

David Keyes 

Fu Foundation Professor of Applied Mathematics 

Department of Applied Physics and Applied Mathematics 

Acting Director, Institute for Scientific Computing Research at LLNL 

Columbia University 
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Alan Laub 

Director, Institute for Digital Research and Education 

Professor, Electrical Engineering / Mathematics 

University of California – Los Angeles 

Linda Petzold 

Chair, Department of Computer Science 

Professor, Department of Mechanical and Environmental Engineering 

Professor, Department of Computer Science 

Director, Computational Science and Engineering Program 

University of California, Santa Barbara 

David Srolovitz 

Department Chair, Mechanical and Aerospace Engineering 

Professor, Mechanical and Aerospace Engineering 

Professor, Princeton Institute for the Science and Technology of Materials 

Princeton University 

Sidney Yip 

Professor, Nuclear Engineering and Materials Science Engineering 

Computational and Systems Biology (CSBi) 

Massachusetts Institute of Technology 

Panel Administrative Coordinator: 

 Jon Bass 

 Deputy Director, Institute for Computational Engineering and Sciences 

 The University of Texas at Austin 
 ix

PREFACE 

This document is the final report of the findings and recommendations of the 

Blue Ribbon Panel on Simulation-Based Engineering Science. The report 

contains recommendations critical to the acceleration of advances in SimulationBased Engineering Science (SBES), and it identifies several areas in which SBES 

can play a remarkable role in promoting developments vital to the health, 

security, and technological competitiveness of the nation. 

For over a decade, the nation's engineering and science communities have 

become increasingly aware that computer simulation is an indispensable tool for 

resolving a multitude of scientific and technological problems facing our country. 

To define the field of computer simulation more precisely and to assess its 

potential impact on important areas of engineering science, in April 2004 the 

NSF organized a workshop on SBES. Encouraged by the widespread interest in 

the results of the workshop, the Foundation appointed a Blue Ribbon Panel on 

Simulation-Based Engineering Science. The purpose of the Panel was to explore 

the challenges and potential benefits of SBES, as well as the barriers to its 

development. Furthermore, the Panel was tasked with making recommendations 

on how the discipline could be nurtured within academia, industry, national 

laboratories, and government agencies. A second workshop on SBES was held in 

September 2005, at which time the Panel received input on SBES from a broad 

constituency. 

Acknowledgements: The Panel has benefited from the advice and council of 

many individuals. The enthusiastic support of Drs. Richard Buckius and Ken 

Chong of NSF and Dr. John Brighton, formerly of NSF, is gratefully 

acknowledged. Valuable advice from the over 100 workshop attendees has also 

been factored into the findings and recommendations. In addition, a large group 

of experts outside the workshops have provided valuable commentary on the 

early drafts of this report. 

The Panel gratefully acknowledges the following individuals and institutions 

for providing the graphics that appear in this report: NASA’s Earth Observatory 

(Cover Image); Charles Taylor, Stanford University (Figure 1); Y. Zhang and C. 

Bajaj, University of Texas and the New York University School of Medicine, 

respectively (Figure 2); SCI, University of Utah (Figure 3); SCI, University of 

Utah, and J. Bell and V. Beckner, Lawrence Berkeley National Laboratory 

(Figure 4); and the U.S. Department of Energy (Figure 5, a reproduction from the 

SCaLeS report). Particularly helpful in the preparation of this final report were 

the comments and advice of Drs. Chandrajit Bajaj, Larry Biegler, Mark 
 x

Carpenter, Alok Chaturvedi, Weng Cho Chew, Frederica Darema, Omar Ghattas, 

George Hazelrigg, Craig Henriquez, Anthony Ingraffea, Kirk Jordan, Chandrika 

Kamath, Dimitri Kusnezov, Wing Kam Liu, Robert Moser, Habib Najm, 

Anthony Patera, Mark Rashid, Mark Shephard, Charles Taylor, Elizabeth 

Tornquist, Mary Wheeler, Daniel White, Jacob White, and David Young,. 

Although this report was prepared by a Panel of the National Science 

Foundation, all opinions, findings, and recommendations expressed here are 

those of the Panel and do not necessarily reflect the views of the National 

Science Foundation. 
 xi

 CONTENTS 

PREFACE................................................................................................................ix

CONTENTS............................................................................................................xi

EXECUTIVE SUMMARY.......................................................................................xiii

1.0 SBES: A National Priority for Tomorrow’s Engineering and Science .............1

2.0 THE PAYOFF: Driving Applications and Societal Benefits of SBES................9

 2.1 SBES in Medicine.............................................................................................9

 2.2 SBES in Predictive Homeland Security ........................................................13

 2.3 SBES in Energy and the Environment ..........................................................17

 2.4 SBES in Materials ..........................................................................................18

 2.5 SBES in Industrial and Defense Applications ...............................................22

3.0 CORE ISSUES: Challenges, Barriers, and Opportunities in SBES 

Research ..........................................................................................................29

 3.1 The Tyranny of Scales: The Challenge of Multiscale Modeling and 

 Simulation........................................................................................................29

 3.2 Verification, Validation, and Uncertainty Quantification..............................33

 3.3 Dynamic Simulation Systems, Sensors, Measurements, and 

 Heterogeneous Simulations ...........................................................................37

 3.4 New Vistas in Simulation Software...............................................................40

 3.5 The Emergence of Big Data in Simulation and the Role of 

 Visualization in SBES .....................................................................................44

 3.6 Next-Generation Algorithms and Computational Performance...................49

4.0 THE CRISIS OF THE KNOWLEDGE EXPLOSION: SBES Education for 

 Tomorrow’s Engineers and Scientists............................................................53

5.0 CONCLUSIONS................................................................................................57
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Appendix A: SBES Workshop Attendees..............................................................61

 April 2004 Workshop .............................................................. p .............................61

 September 2005 Workshop.............................................................. p ....................62

BIBLIOGRAPHY.....................................................................................................63
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 EXECUTIVE SUMMARY 

Simulation refers to the application of computational models to the study and 

prediction of physical events or the behavior of engineered systems. The 

development of computer simulation has drawn from a deep pool of scientific, 

mathematical, computational, and engineering knowledge and methodologies. 

With the depth of its intellectual development and its wide range of applications, 

computer simulation has emerged as a powerful tool, one that promises to 

revolutionize the way engineering and science are conducted in the twenty-first 

century. 

Computer simulation represents an extension of theoretical science in that it 

is based on mathematical models. Such models attempt to characterize the 

physical predictions or consequences of scientific theories. Simulation can be 

much more, however. For example, it can be used to explore new theories and to 

design new experiments to test these theories. Simulation also provides a 

powerful alternative to the techniques of experimental science and observation 

when phenomena are not observable or when measurements are impractical or 

too expensive. 

Simulation-Based Engineering Science (SBES) is defined as the discipline 

that provides the scientific and mathematical basis for the simulation of 

engineered systems. Such systems range from microelectronic devices to 

automobiles, aircraft, and even the infrastructures of oilfields and cities. In a 

word, SBES fuses the knowledge and techniques of the traditional engineering 

fields—electrical, mechanical, civil, chemical, aerospace, nuclear, biomedical, 

and materials science—with the knowledge and techniques of fields like 

computer science, mathematics, and the physical and social sciences. As a result, 
 xiv

engineers are better able to predict and optimize systems affecting almost all 

aspects of our lives and work, including our environment, our security and safety, 

and the products we use and export. 

Whereas the use of computer simulations in engineering science began over 

half a century ago, only in the past decade or so have simulation theory and 

technology made a dramatic impact across the engineering fields. That 

remarkable change has come about mainly because of developments in the 

computational and computer sciences and the rapid advances in computing 

equipment and systems. There are other reasons. For example, a host of 

technologies are on the horizon that we cannot hope to understand, develop, or 

utilize without simulation. Many of those technologies are critical to the nation’s 

continued leadership in science and engineering. Clearly, research in SBES is 

quickly becoming indispensable to our country’s security and well-being. 

This report was prepared by a Blue Ribbon Panel on Simulation-Based 

Engineering Sciences, which was convened by the Assistant Director of the 

Engineering Directorate of the National Science Foundation (NSF). The Panel 

was directed to explore opportunities for and potential advances in SBES and to 

make strategic recommendations as to how to structure programs to foster SBES. 

The Panel developed its findings and recommendations from several 

information sources. Among them, interactions with recognized leaders of the 

computational engineering and science communities played an essential role. 

Another important source of information was the work of previous panels and 

committees. The results of those earlier efforts, which have accumulated over the 

last decade, address major issues in the computational and computer sciences. 

The Panel also relied on input from leaders in the computer simulation 

community who participated in the NSF-supported workshops on SBES. Finally, 

the Panel developed its findings and recommendations after thorough discussions 

among its members. 

This report explores the potential impact of advances in SBES on science and 
 xv

technology and identifies the challenges and barriers to further advances in 

SBES. For instance, we must overcome difficulties inherent in multiscale 

modeling, the development of next-generation algorithms, and the design and 

implementation of dynamic data-driven application systems. We must improve 

methods to quantify uncertainty and to model validation and verification. We 

must determine better ways to integrate data-intensive computing, visualization, 

and simulation. Importantly, we must overhaul our educational system to foster 

the interdisciplinary study that SBES requires. The payoffs for meeting these 

challenges are profound. We can expect dramatic advances on a broad front: 

medicine, materials science, homeland security, manufacturing, engineering 

design, and many others. 

For more than a decade, researchers and educators in engineering and science 

have agreed: the computational and simulation engineering sciences are 

fundamental to the security and welfare of the United States. The findings and 

recommendations of this report strongly reinforce that contention. 
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Major Findings 

1. SBES is a discipline indispensable to the nation’s continued leadership in 

science and engineering. It is central to advances in biomedicine, 

nanomanufacturing, homeland security, microelectronics, energy and 

environmental sciences, advanced materials, and product development. There 

is ample evidence that developments in these new disciplines could 

significantly impact virtually every aspect of human experience. 

2. Formidable challenges stand in the way of progress in SBES research. These 

challenges involve resolving open problems associated with multiscale and 

multi-physics modeling, real-time integration of simulation methods with 

measurement systems, model validation and verification, handling large data, 

and visualization. Significantly, one of those challenges is education of the 

next generation of engineers and scientists in the theory and practices of 

SBES. 

3. There is strong evidence that our nation’s leadership in computational 

engineering and science, particularly in areas key to Simulation-Based 

Engineering Science, is rapidly eroding. Because competing nations 

worldwide have increased their investments in research, the U.S. has seen a 

steady reduction in its proportion of scientific advances relative to that of 

Europe and Asia. Any reversal of those trends will require changes in our 

educational system as well as changes in how basic research is funded in the 

U.S. 
 xvii

Principal Recommendations 

1. The Panel recommends that the National Science Foundation and other Federal 

research agencies make changes in their organizational structures to facilitate longrange core funding of SBES. The new Cyberinfrastructure at NSF is envisioned as a 

“portion of cyberspace” where scientists can “pursue research in new ways and with 

new efficiency” by utilizing: 1) high-performance, global-scale networking, 2) 

middleware, 3) high-performance computing services, 4) observation and 

measurement devices, and 5) improved interfaces and visualization services. Serious 

consideration should be given to the feasibility of developing a parallel program in 

SBES that interfaces to multiple divisions of NSF in concert with 

Cyberinfrastructure. Supporting SBES research should certainly be a goal of every 

division within the Directorate of Engineering at NSF, but the realization of the full 

potential of advances in SBES will require support across all directorates and from 

other federal agencies as well. 

2. To maintain our leadership in science and engineering, the Panel recommends a 

minimum increase in NSF funding of $300 million per year over 2005 levels of 

SBES-related disciplines. We cannot maintain our leadership in engineering and the 

engineering sciences without substantial investments in SBES, because simulation is 

a key element in accelerating progress in engineering. Advances in computing speed 

alone or in measurement devices or in networking or interfaces cannot meet the great 

challenges before us without advances in the basic components of SBES. Similar 

observations have been made in the President’s Information Technology Advisory 

Committee (PITAC) report, as well as in the results of several other related studies. 

3. The Panel recommends a long-term program of high-risk research to exploit the 

considerable promise of SBES. The Panel strongly supports the observation made in 

the PITAC report and elsewhere that short-term investments and limited strategic 

planning will lead to excessive focus on incremental research rather than on longrange sustained research with a lasting impact. Progress in SBES will require the 

creation of interdisciplinary teams that work together on leading-edge simulation 

problems. The work of these teams should be sustained for a decade or more to yield 

the full fruits of the investment. 

4. The Panel recommends that NSF underwrite the work of an NRC committee to 

explore the issue of interdisciplinary education in detail and to make 

recommendations for a sweeping overhaul of our educational system. The problem 

of education in SBES-component disciplines, and in multidisciplinary programs in 

general, is large, pervasive, and critically important. The initiative for change will 

not likely come from academia alone; it must be encouraged by the engineering and 

scientific leadership and throughout the organizational structure of our universities. 

Other important findings and recommendations of the Panel are given in the 

body of the report. 
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1.0 SBES: A National Priority for 

Tomorrow’s Engineering and 

Science 

Today the field of computer simulation is on the threshold of a new era. 

Advances in mathematical modeling, computational algorithms, the speed of 

computers, and the science and technology of data-intensive computing have 

prepared the way for unprecedented improvements in the health, security, 

productivity, and competitiveness of our nation. To realize these advances, 

however, we must overcome major scientific, engineering, sociological, and 

educational obstacles. Progress will require significant breakthroughs in research, 

changes in the research and educational cultures of our academic institutions, and 

changes in the organizational structure of our educational system. For the 

engineering fields, advances in computer simulation offer rich possibilities. Full 

exploitation of the new capabilities, however, must await basic research into the 

scientific components of modeling, simulation, and computing, among other 

areas. We refer to the combination of these basic research activities as 

Simulation-Based Engineering Science, or SBES. 

In this report we describe this new discipline. We first identify some of the 

remarkable benefits SBES brings to technologies that make our lives healthier, 

safer, and better. Next, we survey the core issues of SBES, that is, the major 

obstacles to and opportunities for its development. We then explore the impact of 

SBES on our national research and educational resources, goals, and 

organizations. Throughout the report, we highlight our findings and 

recommendations. In making those recommendations, we attempt to reflect the 

views prevailing within the nation’s scientific and engineering communities. 
 2

This much is certain: there is overwhelming concurrence that simulation is 

key to achieving progress in engineering and science in the foreseeable future. 

Indeed, seldom have so many independent 

studies by experts from diverse perspectives 

been in such agreement: computer simulation 

has and will continue to have an enormous 

impact on all areas of engineering, scientific 

discovery, and endeavors to solve major 

societal problems. This message is woven into 

the principal findings of many key 

investigations. For example, the PITAC report 

[4] emphasizes the need to develop 

computational science for national 

competitiveness, and the SciDAC [26] and SCaLeS [18, 19] reports identify 

opportunities for scientific discovery at the high end of today’s simulation 

capabilities and call for a new scientific culture of interdisciplinary teamwork to 

realize those capabilities. In addition, the Roadmap for the Revitalization of 

High-End Computing [24] and the Federal Plan for High-End Computing [13] 

both call for innovations in computer architecture to accommodate advanced 

simulation. The Future of Supercomputing report [12] examines the role of the 

Federal Government in sustaining the leading edge of supercomputing, and the 

Cyberinfrastructure report [1] outlines a diverse program of interrelated research 

imperatives stretching well beyond simulation into communication and data 

technologies. Beginning with the Lax report of 1982 [20] and continuing with a 

number of reports already appearing in this young century [3, 8, 9, 23], the 

studies share a similar vision regarding the importance of simulation. 

Consequently, the present report enters an arena already filled with voices 

calling for more vigorous research and training in computation-based simulation. 

The ideas in this report are in harmony with those voices, and in fact the report is 

as brief as it is because others have already eloquently articulated the case for 

Seldom have so many 

independent studies been 

in such agreement: 

simulation is a key 

element for achieving 

progress in engineering 

and science. 
 3

simulation. Even so, this report addresses important elements of simulation that 

have been overlooked. Moreover, it adds the voice of engineering to the 

discussion, one that has not yet been fully heard. 

Realizing the full potential of SBES will require a revolution in simulation 

technology. Simulation-Based Engineering Science is not “simulation as usual”; 

rather, it is research focused on the modeling 

and simulation of complex, interrelated 

engineered systems and on the acquisition of 

results meeting specified standards of precision 

and reliability. Indeed, the scope of SBES 

includes much more than the modeling of 

physical phenomena. It develops new methods, 

devices, procedures, processes, and planning 

strategies. Not only does it draw on and 

stimulate advances in our scientific 

understanding, it capitalizes on those advances by applying them to challenges in 

the engineering domain. For example, discoveries in SBES have direct 

applications to optimization, control, uncertainty quantification, verification and 

validation, decision-making, and real-time response. In short, SBES will lift 

simulation to a powerful new level, a level where we hope to solve the most 

stubborn problems of modeling, engineering design, manufacturing, and 

scientific inquiry. In fact, so profound are the implications of advanced 

simulation techniques that we can expect SBES to trigger the development of a 

host of aggressive new technologies and to foster significant new scientific 

discoveries. 

Consider, for example, a few of the breakthroughs that SBES offers: (1) the 

means to understand and control multiscale, multi-physics phenomena; 

(2) fundamental developments in nanotechnology, biomedicine, materials, energy 

and environment, and the earth and life sciences; and (3) dramatic enhancements 

to the fidelity and utility of computational predictions. Clearly, SBES ushers in a 

SBES constitutes a new 

paradigm that will be 

indispensable in meeting 

the scientific and 

engineering challenges of 

the twenty-first century. 
 4

technology that not only expands the reach and capability of every field of 

engineering but also promises significant improvements in the health, security, 

competitiveness, and wealth of our nation. 

If we are to reap the benefits of SBES, however, we must first overcome the 

obstacles. First, we must revolutionize the way we conceive and perform 

simulation. This revolution requires that we learn to incorporate new discoveries 

that simplify and enhance multiscale, multidisciplinary simulations. Second, we 

must make significant advances in the supporting technologies, including largescale computing, data management, and algorithms. Third, we must overhaul our 

educational institutions to accommodate the needs of SBES research and 

training. Fourth, we must change the manner in which research is funded and 

conducted in the U.S. 

So far, developments in simulation have ridden the wave of advances in 

hardware and software information technology. Because of these advances, 

simulation has become an increasingly effective tool in traditional science and 

engineering practices. Unlike most theory, which 

posits restricted, idealized systems, simulation 

deals with real systems. For that reason, 

simulation provides unprecedented access to 

real-world conditions. In addition, simulation has 

none of the limitations of experimental designs 

and tests, which are often hampered by cost 

constraints, unrealistic parameter ranges, and 

even restrictions imposed by treaties or health 

and environmental concerns. For these reasons, 

computer simulation is credited with numerous 

triumphs in the twentieth century. It has become 

indispensable, for example, in assessments of 

vehicle crashworthiness. It is fundamental to the generation of predictive models 

of the weather, climate change, and the behavior of the atmosphere. Its 

Simulation has become 

indispensable in 

predictive methods for 

weather, climate change, 

and behavior of the 

atmosphere; and in 

broad areas of 

engineering analysis and 

design. 
 5

importance in broad areas of engineering analysis and design are well known. It 

has become essential to product manufacturing. Its achievements in biomedical 

applications are widely discussed. Systems design in defense, communication, 

and transportation also rely on computer simulation. 

At the heart of these successes, however, are simulation methodologies that 

are decades old, too old to meet the challenges of new technology. In many ways, 

the past successes of computer simulation may be its worst enemies, because the 

knowledge base, methods, and practices that enabled its achievements now 

threaten to stifle its prospects for the future. 

Our nation prides itself in being the leader in computational science and 

simulation theory and technology. Unfortunately, many indicators suggest that 

the United States is quickly losing ground. Particularly in SBES, the country is 

no longer positioned to lead the world over the next few decades. Even today, the 

consequences of falling behind are penetrating deep into our technology and 

economy, as well as jeopardizing our position in the 

global community. 

Our global competitors are well aware of the great 

potential of computer simulation. Throughout Europe 

and Asia, governments are making major investments 

in computing, mathematical and computational 

modeling, algorithms, networking, and generally in 

computational engineering and science. Indeed, these 

nations are building on the technologies that the U.S. 

pioneered in the twentieth century. We are in danger, 

once again, of producing world-leading science but leaving it to our competitors 

to harvest the technological and economic advantages. 

Yet, even our traditional lead in basic research is under threat. According to 

[4, p. 9]: “Since 1988, Western Europe has produced more science and 

engineering journal articles than the United States and the total growth in 

The importance and 

great potential of 

simulation have not 

gone unnoticed by 

our competitors 

around the world. 
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research papers is highest in East Asia (492 percent), followed by Japan (67 

percent), and Europe (59 percent), compared with 13 percent for the United 

States. Worldwide, the share of U.S. citations in scientific papers is shrinking, 

from 38 percent in 1988 to 31 percent in 2001.” In Germany, 36 percent of 

undergraduates receive degrees in science and engineering; in China, 59 percent, 

and in Japan, 66 percent. In contrast; only 32 percent of undergraduates receive 

such degrees in the United States [6, 25, 21]. 

The imbalance is beginning to reveal itself in international trade. “From 1980 

to 2001, the U.S. share of global high-technology exports dropped from 31 

percent to 18 percent, while the share for Asian countries rose from 7 to 25 

percent” [4, p. 8]. Since 2001, the U.S. trade balance in high-tech products has 

been negative. 

The chief global economic competitor of the United States is China. In 2004 

China graduated approximately 498,000 bachelor’s level engineers. By 

comparison, India graduated 350,000 engineers, and the U.S. graduated 70,000 

[6, 25]. The employment of an engineer in China costs roughly one-tenth to onesixth of what it costs in the United States. Some argue, however, that the U.S. 

production of engineers, computer scientists, and information technology 

specialists remains competitive in global markets when like-to-like data from the 

representative countries are compared [11]. Nevertheless, even our competitors 

in SBES believe that SBES research expenditures in Europe and Asia are rapidly 

expanding while they are stagnant or declining in the U.S. 

The key to offsetting those disadvantages is leadership in simulation. The 

U.S. must be in the forefront of efforts to make simulation easier and more 

reliable. We must extend the capabilities of simulation to the analysis of more 

complex systems and the real-time acquisition of real-time data. Simulation must 

no longer be relegated to the peripheries of an engineering student’s skill set; 

instead, it should be a core part of the engineering curriculum, where it plays a 

role in effective pedagogy. 
 7

Computer simulation has become indispensable to the development of all 

other technologies, including microelectronics, advanced materials, 

biotechnology, nanotechnology, pharmaceuticals, medicine, and defense and 

security. Many breakthroughs in these technologies derive from computer 

simulation and simulation-based scientific discovery. Clearly, we must integrate 

computer simulation into engineering education and practice. To do so, however, 

requires great intellectual resources and a national commitment to SBES. 

In Chapter 2, we describe some of the remarkable applications of SBES, all 

of which represent enormous benefits to our society. If we are to realize such 

great potential, however, we must overcome many technical, mathematical, 

scientific, and computational challenges. So great are these challenges that they 

will require a sea change in our approach to engineering and science education. 

Chapter 3 surveys the greatest of those obstacles. In addition, the chapter 

identifies opportunities for broadening the impact of SBES through 

developments in visualization, sensors and image processing, and uncertainty 

quantification. 

Finally, in Chapter 4 we describe the impact of SBES on our educational 

system. Our educational system must change if it is to equip future generations of 

engineering scientists with knowledge of SBES and its value in science and 

technology. 
 8
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2.0 THE PAYOFF: 

Driving Applications and 

Societal Benefits of SBES 

The applications and benefits of SBES are many. This chapter reviews 

some of the important applications of SBES and explores the challenges and 

benefits of each. 

2.1 SBES in Medicine 

 

Most diseases (such as heart disease, cancer, stroke, and respiratory 

diseases) and their treatments (whether surgical, transcatheter, or pharmacologic) 

involve complex physical responses and interactions between biological systems, 

from the molecular to organism scales. Simulation methods can therefore 

dramatically increase our understanding of these diseases and treatments, and 

furthermore, improve treatment. Computational science has already made 

significant inroads in many biomedical domains, most notably genomics and 

proteomics. A current challenge is the application of SBES to clinical medicine 

and to the study of biological systems at the cellular, tissue, and organ scales. 

Medical practice and engineering have many 

similarities. Both are problem-solving disciplines, and 

both require an understanding of complex systems. 

Engineering design processes, however, are based 

upon predicted outcomes. Often, engineering solutions 

Both medical practice 

and engineering are 

problem-solving 

disciplines. 
 10

require the satisfaction of numerous criteria simultaneously. Those solutions 

often require sophisticated computer and analysis technologies. By contrast, 

medical practice uses a “build them and bust them” approach. Historically, the 

paradigm of medicine combines diagnosis and empiricism; that is, physicians use 

various tests to diagnose a medical condition and then plan a treatment or 

intervention based on empirical data and professional experience. Generally, 

medical practice precludes any formal process to predict the outcome of a 

treatment for an individual patient, although there may be some statistical data to 

indicate the success rate of the treatment. 

 

A program in SBES could lead to new 

approaches to medical practice, approaches that 

could be collectively called Simulation-Based 

Medicine. New SBES methodologies, already 

leveraging tremendous advances in medical imaging 

and high-performance computing, would give the 

practice of medicine the tools of modern 

engineering. For example, doctors would be able to 

use simulations—initialized with patient-specific 

Figure 1: Example of a simulation-based medical approach applied to the design of a bypass 

surgery procedure for a patient with occlusive cardiovascular disease in the aorta and iliac 

arteries. Shown from left to right are magnetic resonance image data, a preoperative geometric 

solid model, an operative plan, computed blood flow velocity in the aorta and proximal end of 

bypass, and post-operative image data used to validate predictions. Advances contemplated in 

SBES disciplines could greatly improve these approaches by allowing real-time adaptive 

control of surgical procedures through the combination of simulation tools and imaging 

technologies. 

A program in SBES 

could also lead to 

new approaches to 

medical practice, 

collectively called 

Simulation-Based 

Medicine. 
 11

anatomic and physiologic data—to predict the outcomes of procedures and 

thereby design optimal treatments for individual patients. This ability to predict 

treatment outcomes and design procedures accordingly represents an exciting 

new possibility for medicine. 

Not only could physicians devise better treatments for individual patients, but 

also manufacturers could use SBES methods to predict the performance of their 

medical devices in virtual patients. The physical and animal testing procedures 

currently used prior to human trials have significant limitations in their ability to 

represent variations in human anatomy and physiology. With SBES methods, 

manufacturers could conduct virtual prototyping of medical devices by 

simulating the performances of alternate device designs for a range of virtual 

Figure 2: The modeling of biomedical systems is becoming increasingly 

sophisticated. Here is an example of a three-dimensional, tetrahedral-mesh, 

heart model [27] developed from surface data obtained from the New York 

University School of Medicine [22]. (a) Exterior view. (b) Boundary detection 

represented in wire frame. (c) Cross-sectional view of the mesh. Patientspecific modeling technologies need to be advanced significantly to make the 

vision of Simulation-Based Medicine a reality. The benefits, however, are 

impressive: dynamic models of deformation, blood flow, and fine-scale 

capillary effects may greatly advance cardiovascular medicine. 
 12

patients. In this way, manufacturers would be able to refine their designs for 

different patient conditions. As a result, these virtual clinical trials prior to 

animal and human trials could lead to safer, more effective devices, reduce 

development costs, and shorten time-to-market. 

Manufacturers in pharmaceuticals and biotechnology could also benefit from 

SBES methods. For example, targeted drug-delivery techniques are being used 

increasingly to treat a range of diseases, including heart disease (for example, 

drug-eluting stents), cancer (for example, local chemotherapies), and chronic 

respiratory diseases (for example, therapeutic inhalants). In all those areas of 

innovation, simulation-based methods could be used to model the transport of 

drugs through the circulatory or respiratory systems and to determine the local 

concentrations to use in pharmacokinetic models of drug metabolism. 

Currently, important topics in cancer research are the mechanisms of cell 

adhesion and invasion and signaling pathways. A better understanding of those 

mechanisms is critical to advances in cancer research and neurobiology. The 

developments of multiscale SBES technologies for investigations into cellular 

structures and cell mechanics, as well as the development of novel cellular forcemeasurement devices, will help explain dynamic cellular architectures and the 

mechanism of cell motility. 

Of particular significance is cell motility. To understand cell motility we 

must first understand: (1) the mechanics of the cell interior, and (2) the 

mechanics of the cell-substrate or cell-ECM (extra-cellular matrix) interaction 

force. These complex mechanisms determine cell shape and migration, which in 

turn allow the cell to perform its critical functions, such as wound healing and 

embryonic morphogenesis. SBES technologies hold promise that we can improve 

our understanding of those cellular functions and increase our ability to 

differentiate normal cells from cancer cells. The stakes are high, because the 

invasion of transformed cells into other tissues—the process called metastasis—

is believed to be the precursor of the development of cancer tissue. 
 13

2.2 SBES in Predictive Homeland Security 

 

In the broadest sense, engineering design for security involves the 

development of systems to protect human populations and the artificial and 

natural infrastructures that support them. The systems protect us from a range of 

threats, whether hostile (for example terrorists), environmental (for example, air 

and water pollution), or natural (for example, earthquake or hurricane). The 

protective systems must guard our entire support infrastructure: buildings; 

transportation systems; food, water, and power distribution; communications; and 

waste disposal. 

The methods of SBES can play an important role in the design and 

optimization of these protective systems. Most notably, SBES would allow our 

emergency planners to predict not only the 

consequences of threats (for example, the accidental 

or malicious release of a toxic chemical or 

biological agent), but also the effects of 

countermeasures. With the aid of predictive 

simulations, engineers would be able to design and 

optimize infrastructures that would be impervious to 

a wide range of threats. With the ability to conduct 

real-time simulations, moreover, an emergency team 

would be able to identify the most rational response to a crisis. The World Trade 

Center disaster of September 11, 2001 serves as a tragic example: if real-time 

simulation had been available, the emergency response team would have realized 

the importance of the immediate evacuation of the building complex. 

SBES can give engineers and planners a remarkably large operational view 

of the systems that make up our society. For example, SBES would give us the 

ability to simulate the operation of a whole city as a single system. It is able to do 

so because it integrates multiscale simulations of multiple subsystems and 

SBES will allow the 

prediction of the 

consequences of 

threats and 

countermeasure 

responses. 
 14

processes, such as structural responses, fluid transport of contaminants, power 

distribution, and transportation systems, as well as the response of the human 

population. 

This vision of a “Digital City” would require the acquisition of data of 

unprecedented detail. Not only would the system have to acquire static data, that 

is, data regarding the installed infrastructure, but it would also have to 

continuously acquire dynamic data, that is, data undergoing constant change. 

Dynamic data, for example, would include continuous measurements of air- and 

water-contaminant concentrations; the flow rates of air, water, and effluents; the 

locations and velocities of transportation and other movable assets (for example, 

trains and heavy machinery); and the densities and movements of people and 

automobiles. 

A logical extension of the Digital-City concept is that of the Digital 

Ecosystem, which may be artificial (such as a city) or natural (such as a forest, 

watershed, continent, or even the entire planet). Whatever the scale, the benefits 

are the same: we gain the ability to optimize human activity and infrastructures in 

respect to adverse events or trends, and, through real-time simulation, we are able 

to identify rational responses to crises. 

The concepts and methods of SBES promise to revolutionize the practice of 

urban planning, transportation, structural and environmental engineering, and 

municipal and environmental management. To realize the visions of the Digital 

City and the Digital Ecosystem, however, we must acknowledge that a great 

amount of research must pioneer the way. The following are a few of the areas 

requiring development: 

• Quantitative models of the processes to be simulated must be developed. For 

many of those processes, models of some level of fidelity already exist, or 

they are being developed for narrower engineering purposes. Obvious 

examples are structural models (of buildings and other structures), fluiddynamics models (air and water flows), combustion models (for example, for 
 15

predicting the spread of fires), and transportation models (for example, to 

analyze traffic flow). For other important processes, however, quantitative 

models are rudimentary or nonexistent. For example, we lack sociological 

models that can help us describe or predict the response of populations to 

crises. In addition, we need better models for the evolution of natural 

ecosystems such as forests or lakes. 

• A comprehensive simulation system is required that integrates detailed 

models of a wide range of scales. The comprehensiveness of the simulation 

system is a requirement if SBES applications are to simulate multiscale 

complex systems. Some of the issues are generic, but others are problem 

specific. 

• New models of exceptional fidelity are required. The development and 

validation of such models entail the acquisition of data of extraordinary 

detail. As a result, the development of the Digital City and Digital Ecosystem 

will inevitably put pressure on the experimental sciences and theoretical 

research to meet the demand for copious data. Furthermore, the real-time 

simulation of some applications will drive developments in sensors and the 

communication infrastructure, both of which must support streams of data. In 

addition, we need to develop the simulation techniques that can 

accommodate the data streams. 

• A better understanding of the role of uncertainty is required. Some degree of 

uncertainty is inevitable in the ability of a model to reflect reality and in the 

data the model uses. We need to find ways to interpret uncertainty and to 

characterize its effects on assessments of the probable outcomes. 

The rewards of meeting those challenges are great: enhanced security, 

safety, and convenience of life in the Digital City and the Digital Ecosystem; a 

social infrastructure of unparalleled efficiency; rational responses to natural 

events; and optimal interactions with natural environments. The following 
 16

summarizes some of the major applications. 

• Protection Against Air Contaminants: SBES technologies will detect and 

measure the presence of biological or chemical contaminants in the air and, 

given detailed weather data, identify the likely release location and 

magnitude of the release. The system will then design an optimal response 

plan. 

• Optimization of Infrastructures: SBES technologies will optimize the 

designs of buildings and other infrastructural elements. Such designs would 

be site specific, interact well with natural and man-made surroundings, and 

blend with the urban system of which they are a part. In addition, the designs 

would take into account the effects of the structures both on normal 

operations and on operations during a wide range of large-scale emergencies. 

• Prediction of Long-Term Environmental Impacts: SBES technologies 

will predict the effects of effluents from existing and proposed facilities on 

urban and natural environments. Such predictions would greatly increase the 

reliability and usefulness of environmental impact studies. In addition, they 

would allow planners to minimize the probability of unforeseen deleterious 

events. 

• Optimization of Emergency Responses: SBES technologies would 

optimize emergency responses to fire and explosion (whether accidental or 

intentional). Planning for emergency responses would consider how the 

situation might evolve or escalate (for example, a fire might spread, building 

collapse, or the response intervention itself might adversely affect the 

situation). 

• Optimization of Security Infrastructures for Urban Environments:

SBES technologies would assist in the design and placement of air- and 

water-contaminant sensors and would help in the planning of 

countermeasures, such as contaminant dispersal and flood abatement. 
 17

2.3 SBES in Energy and the Environment 

The energy-related industries rely on modern simulation methods to monitor 

the production of oil reservoirs, plan pollution remediation measures, and devise 

control strategies. Recent advances in simulation-related technologies may raise 

oil-reservoir management to a new level of sophistication. Those advances 

include distributed computing, multi-physics and 

chemistry modeling, parallel algorithms, and methods 

and devices for the dynamic use of well-bore and 

seismic data. In addition, the next generation of oilfield 

simulation tools could exploit developing technologies 

for data-driven, interactive, and dynamically adaptive 

strategies for subsurface characterization and reservoir 

management. Soon we could see multi-resolution 

reservoir models that can be executed on very large, 

distributed, heterogeneous, computational environments. 

Those models, moreover, could be fed data from sensors 

embedded in reservoir fields (for example, permanent 

downhole sensors and seismic sensors anchored on the 

seafloor). Such a model-and-sensors system could provide a symbiotic feedback 

loop between measurement data and computational models. This approach could 

lead to an instrumented oilfield, one that is more efficient, cost effective, and 

environmentally safe. The strategic and economic benefits are enormous: 

• An increase in the volume of oil and natural gas produced from existing 

reservoirs. With our better understanding of existing oil and gas reservoirs, 

we can expect to deplete existing reserves more efficiently and to locate and 

produce bypassed reserves. The additional production could help us reduce 

our dependence on foreign oil. 

• A better understanding of risks and uncertainties in exploration and lower 

An instrumented 

oilfield will result in 

more efficient, costeffective, and 

environmentally 

safer reservoirs, 

with enormous 

strategic and 

economic benefits. 
 18

finding costs. Better models of the subsurface would allow oil and gas 

companies to focus on prospects that offer the best return. As a result, they 

can allocate their capital much more efficiently. 

The new SBES-related technologies have immediate application to other 

areas as well, including environmental remediation and storage of hazardous 

wastes. Again, these new application areas require an integrated and interactive 

simulation framework with multiscale capabilities. The development and use of 

such frameworks require the support of cross-disciplinary teams of researchers, 

including geoscientists, engineers, applied mathematicians, and computer 

scientists. 

 

 

2.4 SBES in Materials 

 

SBES-related technology may have its greatest societal impact where 

innovations in modeling and simulation methodologies intersect with innovations 

in materials. Multiscale modeling and simulation are transforming the science 

and technology of new-material development and the improvement of existing 

materials. This transformation is tantamount to 

a shift to a powerful new paradigm of 

engineering science. The new methods enable 

the unprecedented ability to manipulate 

metallic, ceramic, semiconductor, 

supramolecular, and polymeric materials. The 

results are material structures and devices that 

have remarkable physical, chemical, 

electronic, optical, and magnetic properties. 

We can now anticipate the molecular design of 

With SBES, materials 

development becomes a 

unique opportunity for the 

integration of fundamental, interdisciplinary 

knowledge, with technological applications of 

obvious benefit to society.
 19

composite materials with undreamed-of functionalities. Moreover, to reap the 

advantages that SBES technology brings to materials development, researchers 

from many disciplines would have to integrate their knowledge in the materials 

sciences. Such collaboration maximizes the possibilities for developing materials 

of great technological value. 

The principle of materials design is rooted in the correlation of molecular 

structure with physical properties. From those correlations, models can be 

formulated that predict microstructural evolutions. Such models allow the 

researcher to investigate the mechanisms underlying the critical behaviors of 

materials and to systematically arrive at improved designs. 

The use of simulations to uncover structure-property correlations can be 

superior to relying only on experimental data. The reason is that simulation 

provides detailed information regarding the evolved microstructure, as well as 

complete control over the initial and boundary conditions. Another significant 

aspect of SBES, one that makes future materials development more robust, is that 

it links simulation methods across different length and time scales. A great deal 

of progress is being made in the first principles calculations of electronic 

structure and in atomistic simulations. Now progress is also being made in 

connecting these two powerful techniques of probing physical phenomena in 

materials. 

The benefits from new materials development are amply evident in the 

current progress in nanoscience and technology, a world-wide enterprise that can 

be compared to drug design. Because of the multiscale nature of materials 

modeling and simulation, SBES is destined to play a key role in nanoscience. 

SBES provides the capability of linking electronic-structure methods, which are 

necessary for dealing with novel nanostructures and functional properties, with 

atomistic and mesoscale techniques. That linkage ensures that the different 

phases of materials innovation—from design to testing to performance and 

lifetime evaluation—can all be simulated, examined, and optimized. 
 20

The power of multiscale computation can be seen in a number of high-profile 

applications involving the behavior of known materials in extreme environments. 

For example, a problem that has occupied the attention of a sizable community of 

researchers for several years is the characterization of the mechanical behavior of 

plastic deformation in metals at high pressure and high strain rate. The challenge, 

which is relevant to national security, is to conduct multiscale simulation that 

links all of the following: calculation of the core of a dislocation using electronicstructure methods; the modeling of dislocation mobility using molecular 

dynamics simulation; and the determination of constitutive relations for 

continuum-level codes. Multiscale simulation can also help solve problems in the 

development of the structural components of nuclear power reactors. Such 

materials must not only be radiation resistant, but they must have lifetimes of 

over 40 years. 

Even for materials that do not have to stand up to the extreme conditions of 

high pressure and intense radiation, the field of materials innovation is rich with 

challenges to our understanding of the underlying microstructures of the 

materials. By meeting those challenges, we can reap enormous benefits. For 

example, we could generate a molecular model of 

cement, the most widely used substance made by 

humans. Such a model would help us develop a new 

cement with greater creep resistance and environmental 

durability. Similarly, models would help us improve the 

performance of catalysts for fuel-cell electric vehicles. 

We could also improve techniques in oilfield 

exploration, where instrumentation and digital 

management of hydrocarbon reservoirs are issues. In all 

these examples, improvements in materials performance 

would have great impact. 

Everywhere one looks there are problems important 

Everywhere one 

looks there are 

problems important 

to society that 

require optimizing 

the functional 

properties of 

materials through 

control of their 

microstructure. 
 21

to society that require optimizing the functional properties of materials through 

the control of their microstructures. Clearly, SBES will have a long-term impact 

on materials innovation. Three attributes of SBES in particular lead to this 

conclusion. 

• Exceptional Bandwidth: The conceptual basis of materials modeling and 

simulation encompasses all of the physical sciences. It makes no distinction 

between what belongs to physics versus chemistry versus engineering and so 

on. This universality of SBES technology represents a scientific bandwidth 

that is at least as broad as the entire range of multiscale applications in 

science and engineering. In materials modeling and simulation, as in SBES 

more generally, traditional disciplinary barriers vanish; all that matters is “the 

need to know.” 

• Elimination of Empiricism: A virtue of multiscale modeling is that the 

results from both modeling and simulation are conceptually and operationally 

quantifiable. Consequently, empirical assumptions can be systematically 

replaced by physically-based descriptions. Quantifiability allows researchers 

to scrutinize and upgrade any portion of a model and simulation in a 

controlled manner. They can thus probe a complex phenomenon detail by 

detail. 

• Visualization of Phenomena: The numerical outputs from a simulation are 

generally data on the degrees of freedom characterizing the model. The 

availability of this kind of data lends itself not only to direct animation, but 

also to the visualization of the properties under analysis, properties that 

would not be accessible to experimental observation. In microscopy, for 

example, researchers can obtain structural information but usually without 

the energetics. Through simulation, however, they can have both. The same 

may be said of data on deformation mechanisms and reaction pathways. 

These three attributes of SBES, of course, are not restricted to materials 
 22

development; they apply equally well to the other areas of SBES application. In 

this section, however, the focus has been on the application of SBES to materials 

development. The point that emerges is that, aided by SBES technology, 

materials modeling and simulation, or computational materials, is becoming the 

sister science of computational physics and computational chemistry. 

 

 

2.5 SBES in Industrial and Defense Applications 

Simulation is ubiquitous in industry. It plays an essential role in the design of 

materials, manufacturing processes, and products. Increasingly, simulation is 

replacing physical tests to ensure product 

reliability and quality. Fewer tests mean fewer 

prototypes, and the result is a shorter design 

cycle. Steady reductions in design cycles, in 

turn, are crucial to U.S. efforts to remain 

competitive in a world where the pace at which 

new consumer products are being developed is 

increasing every day. The need for shorter 

design cycles also applies to our national 

defense and security. World events are often 

unpredictable; our defense industry must be 

able to design, modify, and manufacture equipment in quick response to military 

and police exigencies. A case in point is the unanticipated need to reinforce 

armored vehicles in Iraq after several such vehicles were destroyed by 

improvised explosive devices. 

The use of simulation has proved effective in some industrial applications. 

For example, in crashworthiness studies the few instances that simulations 

To increase U.S. 

competitiveness, short 

design cycles are crucial if 

we are to keep up with the 

rapid pace of newproducts throughout the 

world. 
 23

replaced testing are frequently cited as success 

stories. Generally, however, simulation has yet to 

play a central role in important industrial and 

defense design applications. The reason is that 

large-scale simulation does not enter into the 

design cycle until its later stages. Model 

preparation, after all, requires a substantial amount 

of time and labor. Often it takes months to prepare 

a model, and even then the model needs to be 

calibrated with tests if the design is substantially 

different from previous designs. 

In addition, the preparation of a model usually 

requires considerable knowledge of and skill in 

finite-element analysis. For that reason, the 

challenge of generating models normally falls on 

engineers with advanced degrees, not designers. 

This separation between simulation and design 

activities results in delays and reduces the 

effectiveness of the simulation. 

The difficulties of simulation are compounded 

when new materials, such as composites and 

metallics, are used in product designs. Before 

simulation can even begin, the new materials must 

be tested extensively to determine their properties. 

Such tests are time consuming, and they lengthen 

the time necessary to prepare and conduct useful 

simulations. 

In addition, simulation capabilities are currently limited in their ability to 

model relevant phenomena. For example, in the simulation of a rear collision 

Figure 3: An example of 

contemporary visualization and 

simulation capabilities. Shown is a 

simulation of the explosion of a 

steel tank filled with explosive 

subjected to a jet of heated air. 

New methods are needed to 

capture multiscale effects and 

quantify uncertainties. 
 24

between two automobiles, it would be desirable to model the gasoline in the fuel 

tank and the effects of any fracture of the fuel tank. To date, such multi-physics 

simulations are not possible. Similarly, we are still unable to model the fracture 

of the interior panels and trim, which is important in determining occupant 

injury, or the fracture of sheet metal and structural members, which is common 

with aluminum. 

Thus, crashworthiness simulation technology 

is usually used to check designs near their final 

stages. Optimization of a design for 

crashworthiness in the early stages of the design 

process is simply not yet feasible. The complexity 

of model generation and the uncertainties are too 

great. Even if an industry were to replace testing 

almost completely with simulation, the changeover 

would still require robust verification and 

validation procedures to ensure the effectiveness of 

the simulation tools. 

Similar limitations are found in many other industrial applications where, at 

least superficially, simulation has appeared to be a success. For example, in the 

simulation of sheet-metal forming, important phenomena, such as the tribology 

of the metal-die interface, are not modeled. Those phenomena are characterized 

by much smaller scales than the overall process, and they are subject to great 

variability because they depend on factors such as temperature and the age of the 

lubricant. In the tire industry, simulation is usually limited to determining the tire 

footprint; important performance characteristics such as pothole impact, 

hydroplaning, and cornering performance cannot be simulated because they 

involve multi-physics and many disparate length and time scales. 

In the chemical processing industry, simulation would seem to have a 

significant role. In fact, most petrochemical plants use steady-state process 

If an industry is to 

replace testing with 

simulation, the 

simulation tools must 

undergo robust 

verification and 

validation procedures 

for effectiveness. 
 25

simulation models to perform detailed real-time optimization. As a result, 

chemical plants are more energy and environmentally efficient. Even in the 

chemical industries, however, the use of simulation is limited. Current planning 

models capture only nominal plant capacities; they do not address the real 

performance of the plants and the processes at various scales, nor are they 

sensitive to uncertainties. 

Over the past two decades the integrated circuit industry has been a major 

player in simulation-based engineering. Until now, the U.S. has been a leader in 

the development of highly integrated, easy-to-use software for circuit analysis, 

such as SPICE, and in the education of our engineering workforce in the 

application of that software. As a result, the U.S. was able to maintain its 

leadership in the high value-added segment of the market. As clock rates move 

into the gigahertz range, however, circuit theory is no longer applicable. Future -

generation transistors, such as single electron transistors, low-threshold 

transistors, and quantum computing devices, will be based on new physics that 

links quantum mechanics and electromagnetics. 

Overall, simulation in industry has yet to meet its full potential. The 

following list is a summary of its current limitations: 

1. The development of models is very time consuming, particularly for 

geometries of complex engineering systems such as ships, automobiles, and 

aircraft. Moreover, the determination of material properties often requires 

extensive small-scale testing before simulation can be started, especially if 

statistical properties are needed. This testing also lengthens the time to obtain 

a simulation and hence the design cycle. 

2. Methods are needed for linking models at various scales and simulating 

multi-physics phenomena. 

3. Simulation is often separate from the design optimization process and cannot 

simultaneously deal with factors such as manufacturability, cost, and 

environmental impact. 
 26

Overcoming these barriers will require progress in our basic understanding 

and in the development of powerful new methods. Among these challenges are 

the following: 

1. Multiscale methods that can deal with large ranges of time and spatial scales 

and link various types of physics. 

2. Methods for computing macroscopic phenomena, such as material properties 

and manufacturing processes, in terms of subscale behavior. 

3. Effective optimization methods that can deal with complex integrated 

systems, account for uncertainties, and provide robust designs. 

4. Frameworks for validation, verification, and uncertainty qualification. 

5. Methods for rapidly generating models of complex geometries and material 

properties. 

Multiscale methods will provide extensive benefits. For instance, they will 

enable us to understand relationships and interactions of phenomena at different 

scales, which is crucial in the design of many products. In the design of products, 

it is often necessary to couple diverse physics, such as fluids with solids or 

electromagnetics with structures. Simulations of such couplings involve a large 

range of length and time scales. For example, to model live fire testing of defense 

products, it is necessary to incorporate phenomena of an immense range of scales 

associated with combustion and structures. Similarly, designs involving different 

components also have an enormous range of coupled time scales that determine 

overall system behavior. 

Multiscale methods may also make possible the prediction of material 

properties in terms of basic building blocks, ranging from matrix and fiber 

properties of composite materials to the properties of atoms in metals. Demand is 

increasing for materials that reduce weight and cost; consequently, the 

availability of tools that, through simulation, can predict the behavior of a 

material from its basic building blocks would open tremendous possibilities for 
 27

quickly developing better, less costly, and safer products. Such tools would 

eliminate the bottleneck of extensive materials testing, resulting in substantial 

reductions in design-cycle times. The methods we envision would be able to link 

models of different scales, such as models of micromechanics or even quantum 

mechanics linked to models of macroscale behavior. 

Of course, as design processes increasingly rely on computer simulation, 

validation and verification procedures will become increasingly important. 

Although some efforts have been made at providing validation benchmark 

problems for linear analysis, nonlinear simulation software has not been 

subjected to extensive validation procedures. In fact, we find considerable 

controversy as to what appropriate validation procedures are, how broadly they 

apply, and whether they are even feasible. Clearly, a basic understanding of 

verification and validation procedures is urgently needed. After all, to be useful, 

the simulation tools used by industry and defense agencies must provide reliable 

results. Furthermore, since many real-world phenomena 

are not deterministic, statistical methods that can 

quantify uncertainty will be needed. 

Design optimization is also in its infancy, and it too 

has many obstacles to overcome. The constraints on the 

optimization of a product design relate to 

manufacturability, robustness, and a variety of other 

factors. Optimality often needs to be defined in terms of 

complex criteria, and the frameworks currently in use 

are not readily amenable to that task. Moreover, to be 

effective for engineering design, optimization methods 

must be closely coupled with simulation techniques. 

Generally, however, we still lack a fundamental understanding of what 

constitutes an optimal design and how to find it in a complex multi-criteria 

design environment. Once optimization methods are developed that can deal with 

these complexities, we can expect to see chemical plants, automobiles, laptop 

SBES has the 

potential to deliver, 

within a short design 

period, designs that 

are optimized for 

cost performance 

and total impact on 

the environment. 
 28

computers, and a host of other industrial and consumer products that feature 

unprecedented efficiency at lower cost. 

In summary, SBES has the potential to deliver designs that are optimized for 

cost performance and their total impact on the environment (from production to 

disposal or recycling), all within a short design cycle. This achievement is not 

possible, however, simply by extending current research methods and taking 

small, incremental steps in SBES development. The barriers to the realization of 

SBES relate to our entire way of conducting research and development and 

educating engineers. The next chapter discusses some of these core issues. 

Finding

Because of the interdisciplinary character and complexity of SBES 

challenges, incremental, short-term research efforts are inadequate to achieving 

SBES goals. Instead, a long-term program of high-risk research will be needed to 

resolve the numerous obstacles standing in the way of SBES developments. The 

Panel agrees with the observation made in the PITAC report and elsewhere that 

short-term investments and limited strategic planning will lead to an excessive 

focus on incremental research rather than on the long-range, sustained research 

necessary to have a lasting impact. Moreover, progress in such research will 

require the creation of interdisciplinary teams that work together on leading-edge 

simulation problems. The work of those teams should be sustained for a decade 

or more for the investment to yield its full fruits. 
 29

3.0 CORE ISSUES: Challenges, 

Barriers, and Opportunities in 

SBES Research 

All of the driving applications discussed in the preceding chapter share 

common challenges, barriers, and requirements for research breakthroughs. We 

elaborate on the major issues in this chapter. 

3.1 The Tyranny of Scales: The Challenge of 

 Multiscale Modeling and Simulation 

Researchers in the worldwide race toward miniaturization, nanoscience, 

molecular modeling of drugs and biological systems, advanced materials, and 

other applications, all of which involve events on atomistic and molecular levels, 

have run into a formidable roadblock: the tyranny of scales. Virtually all 

simulation methods known at the beginning of the twenty-first century were valid 

only for limited ranges of spatial and temporal scales. Those conventional 

methods, however, cannot cope with physical phenomena operating across large 

ranges of scale—12 orders of magnitude in time scales, such as in the modeling 

of protein folding [4, p. 4], or 10 orders of magnitude in spatial scales, such as in 

the design of advanced materials. At those ranges, the power of the tyranny of 

scales renders useless virtually all conventional methods. Confounding matters 

further, the principal physics governing events often changes with scale, so that 

the models themselves must change in structure as the ramifications of events 
 30

pass from one scale to another. 

The tyranny of scales dominates simulation efforts not just at the atomistic or 

molecular levels, but wherever large disparities in spatial and temporal scales are 

encountered. Such disparities appear in virtually all areas of modern science and 

engineering, for example, in astrophysics, atmospheric science, geological 

sciences, and in the design of complex engineering systems such as submarines, 

commercial aircraft, and turbine engines. 

In many ways, all that we know about the physical universe and about the 

design and functioning of engineering systems has been partitioned according to 

categories of scale. The designer manipulating the 

electronic properties of materials sees the world as a 

myriad of infinitesimal atoms with clouds of orbiting 

electrons. The atmospheric scientist sees the world as 

the movement of great air masses that change 

climate conditions across thousands of miles of the 

earth’s surface. Today, we are attempting 

technological advances that cannot tolerate any view 

of nature that partitions phenomena into neat 

categories of scale. The modeling and simulation 

tools we are seeking now must be commensurate in 

their applications with the great breadth of the 

phenomena they must simulate. 

The tyranny of scales will not be defeated simply 

by building bigger and faster computers. Instead, we 

will have to revamp the fundamental ways we 

conceive of scientific and engineering methodologies, long the mainstays of 

human progress. Such a daunting challenge, historic in its significance, is beyond 

the capability of single individuals and disciplines. The necessary breakthroughs 

in computational mathematics and the development of new ways to model 

The development of 

effective multiscale 

modeling techniques 

will require major 

breakthroughs in 

computational 

mathematics and new 

thinking on how to 

model natural events 

occurring at multiple 

scales. 
 31

natural events at multiple scales will require the efforts of interdisciplinary teams 

of researchers and thinkers working in concert. 

We can see instances already where preliminary and often ad hoc 

methodologies have retarded technological progress. The design of nanodevices 

is one example. Nanodevices are systems with tiny masses and relatively large 

surface areas. The design of such devices is in urgent need of new simulation 

tools, because they are at a scale too small to be captured by continuum 

mechanics. Another example is any biological application that requires methods 

that link diverse time scales. For instance, the sequence of events following a 

medical implant is initiated by the interactions between individual water 

molecules and the surface of the implant. This first set of interactions occurs on a 

timescale of nanoseconds. The resulting water “shell,” in turn, has an influence 

on proteins and other types of molecules that arrive later. This second set of 

interactions has a timescale from microseconds to milliseconds. Thus, there is a 

significant time gap in the behavior that is difficult to model. Similarly, temporal 

gaps in the behavior of polymers cause problems for current simulation methods. 

Numerous important applications of nanotechnology are being driven by 

homeland security. As a result, the need and urgency for developing multiscale 

tools has increased significantly. For example, we know that small concentrations 

of chemical and biological agents can have lethal effects on large segments of the 

human population. The recognition of that threat has prompted development of 

new mitigation methods, such as miniaturized intelligent sensors, protective 

clothing, and masks. The need for engineered nanostructured materials for 

homeland-security applications, as well as for optical and structural applications, 

has spurred much interest in the development of multiscale methods that can 

accommodate diversity in spatial scales. 

Recent work in multiscale modeling has emphasized the synthesis of theories 

applicable to different scale ranges, such as quantum, molecular, and continuum 

descriptions [5, p. 1504]. Nevertheless, enormously important technological 
 32

problems, such as turbulence modeling, remain unsolved. These problems 

involve a very broad range of scales amenable to a single description, such as 

continuum theory in the case of turbulence. In fact, turbulent-flow problems in 

practical engineering involve such an enormous range of scales that they cannot 

be currently solved on the world’s largest and fastest computers. If we assume 

that progress continues at the rate of Moore’s Law, the turbulence-flow problems 

will not succumb to solutions for many generations to come. The implications of 

solving these problems are great; for example, they have to do with our 

leadership in designing future generations of commercial and military aircraft. 

Before we can lead, however, we must find the path to fundamental 

developments in multiscale modeling. 

The urgency of the development of multiscale simulation models has been 

felt worldwide. Over the past five years, virtually every conference, symposium, 

and international congress devoted to computational engineering and science has 

listed multiscale modeling as an important theme. Multiscale modeling has often 

been the subject of colloquia, study groups, or invited lectures. Three DOE 

workshops were recently held on multiscale mathematics [8, 23, 10], and 

programs at NSF and NIH are already in place for promoting the beginnings of 

new research in this area. In recent years, a large and growing body of literature 

in physics, chemistry, biology, and engineering has focused on various methods 

to fit together simulation models of two or more scales, and this has led to the 

development of various multi-level modeling approaches. To date, however, 

progress on multiscale modeling has been agonizingly slow. Only a series of 

major breakthroughs will help us establish a general mathematical and 

computational framework for handling multiscale events and reveal to us the 

commonalities and limitations of existing methods. 
 33

Finding

Formidable obstacles remain in linking highly disparate length and time 

scales and in bringing together the disciplines involved in researching simulation 

methods. These issues are common to many SBES applications. Fundamental 

discoveries will be needed to surmount these obstacles. 

3.2 Verification, Validation, and 

Uncertainty Quantification 

The ultimate goal of simulation is to predict physical events or the behaviors 

of engineered systems. Predictions are the basis of engineering decisions, they 

are the determining factor in product or system design, they are a basis for 

scientific discovery, and they are the principal reason that computational science 

can project itself beyond the realm of physical experiments and observations. It is 

therefore natural to ask whether specific decisions can 

rely on the predicted outcomes of an event. How 

accurate are the predictions of a computer simulation? 

What level of confidence can one assign a predicted 

outcome in light of what may be known about the 

physical system and the model used to describe it? 

The science, technology, and, in many ways, the 

philosophy of determining and quantifying the 

reliability of computer simulations and their 

predictions has come to be known as V&V, or 

verification and validation. The methods of V&V are 

fundamental to the success and advancement of SBES. 

To appreciate the subtleties and goals of V&V, one must first dissect the 

What level of 

confidence can one 

assign a predicted 

outcome in light of 

what may be known 

about the physical 

system and the model 

used to describe it? 
 34

process of simulation. Beginning with the conceptual understanding of certain 

physical events of interest and with scientific theories that explain them (the 

target physical phenomena or engineering system identified for study), the 

analyst (the modeler, scientist or engineer) constructs a mathematical model of 

the event. The mathematical model is a collection of mathematical constructions, 

equations, inequalities, constraints, etc., that represent abstractions of the reality, 

and are dictated by the theory or theories characterizing the events. The analyst 

then develops a computational model of the event. The computational model is a 

discretized approximation of the mathematical model, and its purpose is to 

implement the analysis on a computer. Validation is the subjective process that 

determines the accuracy with which the mathematical model depicts the actual 

physical event. Verification is the process that determines the accuracy with 

which the computational model represents the mathematical model. In simple 

terms, validation asks, “Are the right equations solved?” while verification asks, 

“Are the equations solved correctly?” 

The entire field of V&V is in the early stage of development. Basic 

definitions and principles have been the subject of much debate in recent years, 

and many aspects of the V&V remain in the gray area between the philosophy of 

science, subjective decision theory, and hard mathematics and physics. The 

twentieth century philosopher of science Karl Popper asserted that a scientific 

theory could not be validated; it could only be 

invalidated. Inasmuch as the mathematical model of 

a physical event is an expression of a theory, such 

models can never actually be validated in the strictest 

sense; they can only be invalidated. To some degree, 

therefore, all validation processes rely on prescribed 

acceptance criteria and metrics. Accordingly, the 

analyst judges whether the model is invalid in light 

of physical observations, experiments, and criteria 

The most 

confounding aspect of 

V&V has to do with 

uncertainty in the data 

characterizing 

mathematical models 

of nature. 
 35

based on experience and judgment. 

Verification processes, on the other hand, are mathematical and 

computational enterprises. They involve software engineering protocols, bug 

detection and control, scientific programming methods, and, importantly, a 

posteriori error estimation. 

Ultimately, the most confounding aspect of V&V has to do with uncertainty 

in the data characterizing mathematical models of nature. In some cases, 

parameters defining models are determined through laboratory tests, field 

measurements, or observations, but the measured values of those parameters 

always vary from one sample to another or from one observation to the next. 

Moreover, the experimental devices used to obtain the data can introduce their 

own errors because of uncontrollable factors, so-called noise, or errors in 

calibration. For some phenomena, little quantitative information is known, or our 

knowledge of the governing physical processes is incomplete or inaccurate. In 

those cases; we simply do not have the necessary data needed to complete the 

definition of the model. 

Uncertainty may thus be due to variability in data due to immeasurable or 

unknown factors, such as our incomplete knowledge of the underlying physics or 

due to the inherent nature of all models as incomplete characterizations of nature. 

These are called subjective uncertainties. Some argue that since the data itself 

can never be quantified with absolute certainty, all 

uncertainties are subjective. Whatever the source of 

uncertainty, techniques must be developed to 

quantify it and to incorporate it into the methods and 

interpretation of simulation predictions. 

Although uncertainty-quantification methods 

have been studied to some degree for half a century, 

their use in large-scale simulations has barely 

begun. Because model parameters can often be 

The use of stochastic 

models can represent 

gigantic increases in 

complexity in data 

volume, storage, 

manipulation, and 

retrieval requirements.
 36

treated as random fields, probabilistic formulations provide one approach to 

quantifying uncertainty when ample statistical information is available. The use 

of stochastic models, on the other hand, can result in gigantic increases in the 

complexity of data volume, storage, manipulation, and retrieval requirements. 

Other approaches that have been proposed for uncertainty quantification include 

stochastic perturbation methods, fuzzy sets, Bayesian statistics, information-gap 

theory, and decision theory. The development of reliable methodologies—

algorithms, data acquisition and management procedures, software, and theory—

for quantifying uncertainty in computer predictions stands as one of the most 

important and daunting challenges in advancing SBES. 

Finding

While verification and validation and uncertainty quantification have been 

subjects of concern for many years, their further development will have a 

profound impact on the reliability and utility of simulation methods in the future. 

New theory and methods are needed for handling stochastic models and for 

developing meaningful and efficient approaches to the quantification of 

uncertainties. As they stand now, verification, validation, and uncertainty 

quantification are challenging and necessary research areas that must be actively 

pursued. 
 37

3.3 Dynamic Simulation Systems, Sensors, 

Measurements, and Heterogeneous Simulations 

 

One of the most challenging applications of SBES, but one that may yield the 

greatest dividends, is the linkage of simulation tools directly to measurement 

devices for real-time control of simulations and computer predictions. Some 

preliminary investments in the research into this new idea have been made under 

NSF’s program in dynamic data-driven applications systems (DDDAS) [7, 9]. 

The full development of this revolutionary and fundamentally important 

discipline will take years of research and technological development. 

The concept of DDDAS is envisioned as a new paradigm in computer 

simulation, one involving a “symbiotic feedback control system” [7, 9] in which 

simulations and experiments (or field data) interact in real time to dramatically 

improve the fidelity of the simulation tool, its accuracy, and its reliability. 

The document that originally put forth the idea [7], now over five years old, 

described the goal of DDDAS as one of developing “application simulations that 

can dynamically accept and respond to ‘online’ field data and measurements 

and/or control such measurements. This 

synergistic and symbiotic feedback control loop 

among applications, simulations, and 

measurements is a novel technical direction that 

can open new domains in the capabilities of 

simulations with a high potential payoff, and 

create applications with new and enhanced 

capabilities. It has the potential to transform the 

way science and engineering are done, and 

induces a major beneficial impact in the way 

many functions in our society are conducted, such 

This synergistic and 

symbiotic feedback 

control loop among 

applications, simulations,

and measurements has 

the potential to transform 

the way science and 

engineering are done. 
 38

as manufacturing, commerce, transportation, hazard prediction/management, and 

medicine, to name a few.” 

A half-decade later, these words are still true, but we also better appreciate 

the size of the challenge. To develop DDDAS, we must resolve issues involving 

the complexity of the systems, the breadth of expertise and technologies required 

to implement the systems, the new software infrastructures, and the efficiency 

and capacity of the computational and data management systems required. 

Success on all those technological fronts will mandate a sustained and wellfunded program of basic and applied research over possibly a decade or more. 

The payoffs, however, are immense—so important, in fact, that the highest 

priority should be given to developing and exploiting this fundamental SBES 

discipline. DDDAS is a concept conceived, defined, and promoted in the United 

States. To capitalize on our own initiatives, however, we must become aggressive 

in our development of DDDAS. Our current complacency in this technology is 

allowing our competitors to gain on us, once again. 

The basic building blocks of DDDAS include the following: 1) a hierarchy of 

heterogeneous simulation models, 2) a system to gather data from archival and 

dynamic sources, 3) algorithms to analyze and predict system behavior by 

blending simulation models and data, 4) algorithms to steer and control the data 

gathering and model validation processes, and 5) the software infrastructure 

supporting model execution, data gathering, analysis prediction, and control 

algorithms. 

In many ways, DDDAS will rewrite the 

book on the validation and verification of 

computer predictions. No longer will 

validation be a one-shot operation to judge the 

acceptability of a simulation problem on the 

basis of a static data set. In DDDAS, validation 

becomes a part of the dynamic control process 

Dynamic data-driven 

application systems will 

rewrite the book on the 

validation and verification 

of computer predictions. 
 39

that identifies and assesses deficiencies of the computational model and upgrades 

and improves the model on the fly. This incorporation of validation into the 

dynamics of the model dramatically enriches the predictability of the model and 

increases confidence in the predicted results. 

DDDAS dynamically incorporates measurement data into a simulation as 

that simulation is executing. The simulation, in turn, dynamically steers the 

measurement process. To perform these operations, DDDAS integrates largescale numerical computing with data-intensive computing, sensors, imaging, grid 

computing, and other measurement devices. The development of this technology 

requires new concepts in software infrastructure, algorithms, control protocols, 

and solvers. Once in place, however, the technology offers an endless list of 

applications. Surgical procedures, homeland security, control of hazardous 

materials, environmental remediation, drug delivery, manufacturing processes, 

oil reservoir management, and vehicle flight control are just a few. 

 

 

Finding

Research is needed to effectively use and integrate data-intensive computing 

systems, ubiquitous sensors and high-resolution detectors, imaging devices, and 

other data-gathering storage and distribution devices, and to develop 

methodologies and theoretical frameworks for their integration into simulation 

systems. Concomitant investments are also required in sensory-data computing, 

the collection and use of experimental data, and the facilitation of interactions 

between computational models and methods, all of which are necessary to 

achieve dynamic adaptive control of the computational process. 
 40

3.4 New Vistas in Simulation Software 

 

Many contemporary engineering communities regard simulation software as 

a commodity that vendors provide for well-defined, specific, and independent 

domains of application. Occasionally, these long-lived codes for engineering 

analysis receive incremental improvements, usually in the form of functional 

extensions. This leisurely approach to software development will not support the 

next generation of engineering problems—multiscaling with real-time data 

interaction and abundant uncertainties in the data. As the PITAC report states 

[14], “Today it is altogether too difficult to develop computational science 

software and applications. Environments and toolkits are inadequate to meet the 

needs of software developers in addressing increasingly complex 

interdisciplinary problems. Legacy software remains a persistent problem 

because the lifetime of a computational science application is significantly 

greater than the three- to five-year life cycle of a computing system. In addition, 

since there is no consistency in software engineering best practices, many of the 

new applications are not robust and cannot easily be ported to new hardware.” 

For those reasons, entirely new approaches are 

needed for the development of the software that will 

encapsulate the models and methods used in SBES. 

Researchers must identify the methodologies that support 

the interoperability of individual components of 

simulation software. Then they must develop those 

methodologies and integrate them into the next 

generation of engineering software. This search for new 

methods and tools to support simulation software 

development is fraught with difficulties. Not only do the 

new simulation components require complex algorithms, 

they must also function efficiently on an evolving range 

Entirely new 

approaches are 

needed for the 

development of the 

software that will 

encapsulate the 

models and 

methods used in 

SBES. 
 41

of architectures designed for large-scale parallel computations. 

Tomorrow’s SBES software requires extraordinary degrees of robustness, 

efficiency, and flexibility. The new software must not only execute simulation 

algorithms, but must also dynamically manage data throughput and model 

adaptivity and control. It must steer observational and measurement systems to 

optimize data collection and use. It must navigate efficiently across models of 

multiple scales and accommodate multiple physical theories, and it must have 

scalable methods that interact seamlessly with data-gathering devices. 

Much of our contemporary software development tools—libraries (for 

instance, linear equation solvers), language interoperability tools, component 

coupling and data transfer tools, and simulation development frameworks—do 

not meet the demands of SBES. To define the real requirements for the 

implementation of SBES technologies, we require a new paradigm of software 

development. Such a fundamental change calls for a great deal of “out-of-thebox” thinking about the way we approach software 

development and practice engineering. The change 

will even affect the way scientific computing is 

taught and perceived in our universities. Not only 

will tomorrow’s software developers have to cope 

with more complex systems and heterogeneous 

hardware systems, but they will also have to 

understand the important details of the applications. 

Whereas the future of SBES software is largely 

uncharted, some path-finding work is under way. A 

Federal government group, for instance, has taken a 

similarly aggressive software philosophy and 

developed software with which to bootstrap. This effort is called the Scientific 

Discovery Through Advanced Computing (SciDAC) initiative [26] (see also 

Chapter 2 of [18]). Organized in 2001, SciDAC is a highly interdisciplinary 

Not only will tomorrow’s 

software developers 

have to cope with more 

complex algorithms, 

but they will also have 

to understand the 

important details of the 

applications.
 42

program tasked with finding methods by which state-of-the-art mathematics and 

domain-specific application sciences can be embodied in robust codes that run 

efficiently on current terascale supercomputers. The initiative is driven by 

research into science applications relevant to the goals of DOE’s Office of 

Science. Those applications include fields such as global climate modeling, 

plasma fusion, quantum chromodynamics, accelerators, combustion, and 

supernovae, as well as the mathematics and computer science relevant to those 

disciplines. Most of this research portfolio involves the coordinated efforts of 

dedicated domain scientists in collaboration with mathematicians, computer 

scientists, and computational scientists throughout the nation. One of the 

principal products of SciDAC is sharable software, and development of such 

software takes the sustained effort of a permanent staff. For this reason, the 

center of gravity for most of the work in this field is at national labs. In addition, 

U.S. universities continue to play a key role in that research. At any rate, it is 

clear that the questions are too large and complex for any single institution to 

manage alone. Instead, we must encourage the formation of teams of researchers, 

working in a collaborative software environment, where they can profit from 

distributed resources and expertise. 

The DOE’s National Nuclear Security Agency is also heavily involved in 

computational science. In the mid-1990s, that agency embarked on an ambitious 

program of Stockpile Stewardship. Part of the 

program was the Accelerated Strategic Computing 

Initiative (ASCI, now ASC). With ten times the 

funding of SciDAC, the ASCI program, among other 

goals, seeks to use simulation to manage our nuclear 

stockpile. Simulation, in this case, would be a 

substitution for our actual testing of the devices, 

which is prohibited by international treaty. Five 

centers were established at U.S. universities to 

The new paradigm for 

SBES software 

research and 

development will 

allow for 

specialization with 

cross-accountability.
 43

develop the computational science for ASCI. 

As both DOE programs exemplify, if the engineering sciences are to realize 

the full benefits of the rapid advances in computing technologies, we must 

somehow integrate the knowledge and discoveries of mathematics, computer 

science, engineering, and the domain sciences. We also need to recognize that 

SBES is located at the intersection of those disciplines. In that sense, we can 

think of SBES as a super-discipline. 

The new paradigm for SBES software research and development will allow 

for specialization with cross-accountability. As envisioned, mission-driven 

teams, primarily practicing engineers, will define and model the engineering 

systems that require breakthroughs in simulation methods (for example, artificial 

organs or distribution networks). In addition, the engineers will tentatively 

identify data interfaces and the computational tasks between those interfaces. 

From there, teams of “enabling technologists,” primarily mathematicians and 

computer scientists, will tackle the abstract requirements identified by the 

mission-driven teams and develop software components that port across the 

target architectures (for example, massively parallel distributed memory 

computers). The component development will track the research frontier for each 

algorithmic area of expertise (for example, error estimation or eigenanalysis) 

while also conforming to mission requirements. 

Finding

Much of our current software in computational engineering science is inadequate for 

dealing with the multifaceted applications and challenges of SBES. New software tools, 

paradigms, and protocols will need to be developed so that software is more transferable 

between fields and not wastefully duplicated. In the multidisciplinary teams we establish 

for SBES research, we must incorporate experienced software developers who will work 

closely with engineering scientists to develop tomorrow’s SBES software. 
 44

3.5 The Emergence of Big Data in Simulation and 

 the Role of Visualization in SBES 

 

Since the advent of computing, the world has experienced an information 

“big bang,” an explosion of data. Information is being created at an exponential 

rate. Since 2003, digital information makes up 

90 percent of all information production, 

vastly exceeding the amount of paper and film. 

One of the greatest scientific and engineering 

challenges of the twenty-first century is to 

understand and make effective use of this 

growing body of information. 

In the computational engineering and 

science environment that existed near the end 

of the previous century, data-intensive computing and large-scale scientific 

computing were essentially disjoint camps. One transported, stored, and 

manipulated large data sets, and the other implemented scalable parallel 

computing strategies for resolving very large computational models of scientific 

and engineering problems. That era of separation has passed. In all the 

applications of SBES discussed in this chapter, the use and generation of 

immense data sets are integral components. For example, uncertainty 

quantification, a key component of SBES, will require data sets many orders of 

magnitude larger than those of traditional deterministic computing. DDDAS, by 

definition, will demand new methods that rapidly generate, store, access, and 

transfer large data sets over computational grids or high-bandwidth networks. 

Then there is the issue of interpreting the results of the simulation itself, a 

problem that can involve gigantic data sets. 

As we work to harness the accelerating information explosion, visualization 

will be among our most important tools. Indeed, visualization capabilities will 

The era in which dataintensive computing and 

large-scale scientific 

computing were 

essentially disjoint camps 

is over. 
 45

have a dramatic impact on scientific, biomedical, and engineering research; 

defense and national security; and industrial innovations. 

The reason visualization is such a powerful tool is that it is fundamental to 

our ability to interpret models of complex phenomena, such as multilevel models 

of human physiology from DNA to whole organs, multi-century climate shifts, or 

multidimensional simulations of airflow past a 

jet wing. Visualization reduces and refines data 

streams rapidly and economically, thus enabling 

us to winnow huge volumes of data – an ability 

important in applications such as the 

surveillance of public health at a regional or 

national level in order to track the spread of 

infectious diseases. Visualization for solving 

problems in applications like hurricane 

dynamics and homeland security are generating 

new knowledge that crosses traditional 

disciplinary boundaries. Finally, the use of 

visualization is rapidly transforming business 

and engineering practices for the better [15], 

thereby increasing the competitive edge of our 

industry. 

Visualization allows people to comprehend visual representations of data 

much more rapidly than they can digest the raw numbers or text. The designers 

of computer visualization tools exploit the high-bandwidth channel of human 

visual perception. Software systems may provide either static or interactive 

visual representations of data, depending on user needs and on whether the final 

goal is the explanation or the exploration of the data. 

Visual representation of information has a rich historical tradition, primarily 

in manually created depictions such as anatomical drawings, spread sheets or 

Visualization is 

fundamental to our ability 

to interpret models of 

complex phenomena, 

such as multilevel models 

of human physiology from 

DNA to whole organs, 

multi-century climate 

shifts, or multidimensional 

simulations of airflow past 

a jet wing. 
 46

basic graphics. Now, however, computer graphics has the scalability to handle 

datasets much larger than any that could be manually depicted. In addition, 

computer graphics offers new possibilities in animation and interactivity. 

Visualization is useful for detecting patterns, assessing situations, and prioritizing 

tasks. Computation alone does not lead to understanding. The end user also needs 

a comprehensible interface with the computational output. Visualization provides 

that interface, and in so doing becomes the key to the interpretation of the data. 

Engineers need assistance in making complex decisions and analyses, 

especially with tasks involving large amounts of data. Often, engineers have to 

deal with over-specified situations, and visualization of the situation helps them 

filter out the irrelevant data. Engineers can use visual analysis systems to explore 

“what if” scenarios and to examine data under multiple perspectives and 

assumptions. They can identify connections between any number of attributes, 

and they can assess the reliability of any conclusions [15]. 

Visualization research must continually respond to 

and address the needs of the scientific community. For 

example, the ability to visualize measures of error and 

uncertainty will be fundamental to a better 

understanding of three-dimensional simulation data. 

This understanding will allow the validation of new 

theoretical models, improve the interpretation of data, 

and facilitate decision-making. With few exceptions, 

however, visualization research has ignored the need 

for visual representation of errors and uncertainty for 

three-dimensional visualizations [16]. We need to 

create an SBES visualization framework for 

uncertainty and to investigate new visual 

representations for characterizing error and uncertainty. 

Within DDDAS and SBES applications, visualization of time-dependent data 

We need to create 

an SBES 

visualization 

framework for 

uncertainty and to 

investigate and 

explore new visual 

representations for 

characterizing error 
 47

will be crucial. Currently, however, most interactive visualization techniques 

make use of static data only. The prevailing method for visualizing timedependent data is first to select a viewing angle and then to render time steps offline and play the visualization back as a video. Whereas this approach is often 

adequate for presentational purposes, the inability to engage in interactive 

exploration undermines the effectiveness and relevancy of visualization for 

investigative purposes. Thus, new methods for interactively visualizing largescale, time-dependent data are needed. In addition, we need methods for 

visualizing vector and tensor fields, field data collected experimentally from 

multiple sources, and the ability to visualize data from both a global and local 

perspective. 

Figure 4: State-of-the-art visualization of turbulence in combustible flow. 

Tomorrow’s capabilities may include parallel, interrogative visualization tools, 

integration of visualization with large-scale dynamics simulations of complex 

multi-physics events, and zooming techniques to visualize events at multiple scales. 
 48

 New approaches to algorithmic visualization will be needed that focus on the 

needs of SBES. One approach is interrogative visualization, which is another 

way of saying that quantitative querying through analysis must be supported 

hand-in-hand with fast rendering of domains and computed function fields. A 

second approach is interpretive visualization. Interpretive visualization focuses 

on informatics and techniques to interpret imaging data, as well as various 

quantitative-analysis data. For example, going from imaging data to the 

construction of a domain model is an arduous task. In particular, to capture 

spatial domain realism at each of the desired scales of simulation is daunting, and 

may in fact be impossible. 

A third approach is repetitive visualization. Humans have several 

biorhythms. We have the daily circadian rhythm of mental efficiency, and we 

alternate between periods of work and rest. We should accommodate biorhythms 

in our methodologies for quantitative or interpretive visualization. In other 

words, we should develop a mathematical framework for visualizations that 

allows our perceptions of information to change with repeated visualizations of 

that information. Additionally, if we are simulating the same function using 

models at varying scales, we would have a natural opportunity to revisit our 

earlier visualizations and make comparisons at multiple scales [2]. 

Because of the complexity and the massive amounts of data from 

simulations, researchers will turn to semi-automated techniques from the 

multidisciplinary field of scientific data mining to extract useful information 

from the data. To meet this purpose, data-mining techniques exploit ideas from 

image and video processing, statistics, pattern recognition, mathematical 

optimization, and other fields. Scientific data-mining techniques can be used to 

quantitatively compare simulations to each other and to experiments, to extract 

summary statistics from high-fidelity simulations for use in building models, and 

to analyze experimental data. 

Whereas data-mining techniques can be effective in the extraction of 
 49

information from simulation data, several open challenges remain. Those 

challenges include the extraction of features of interest from adaptive mesh 

refinements and unstructured grids, the processing and interpretation of 

experimental images that are often of low quality, the definition of metrics used 

in comparisons of simulations and experiments, and the analysis of distributed 

data sets resulting from simulations on parallel systems [17]. 

Finding

Visualization and data management are key technologies for enabling future 

contributions in SBES. In addition, they hold great promise for scientific 

discovery, security, economic competitiveness, and other areas of national 

concern. Computer visualization will be integral to our ability to interpret and 

utilize the large data sets generated in SBES applications. 

3.6 Next-Generation Algorithms and 

 Computational Performance 

Algorithms, the recipes for turning mathematical into computational 

processes, provides the bridge between the models describing physical and 

engineered systems, on the one hand, and the computational devices that generate 

the digital representations of simulations, on the other. Too often, only the speed 

of a computational device is cited as the figure of merit for simulation 

performance, and the impact of algorithms on reducing the time complexity 

(number of operations) and space complexity (size of memory) is unappreciated. 

For more than three decades, progress in microprocessor capabilities has been 

described by Moore’s Law, the observation that the number of transistors per unit 
 50

area on a processor doubles every 18 months, with corresponding increases in 

practical performance for a fixed algorithm. Faster and more cost-effective 

hardware is a strong driver for simulation-based engineering. However, 

algorithmic improvements have been far more important. 

In tomorrow’s SBES environment, the computing performance of individual 

microprocessors will be just one of many important factors. New metrics will be 

needed to judge the effectiveness of systems based on SBES principles. More 

fundamental metrics include: time to solution in a multiprocessor environment, 

the wall-clock time that elapses from the initiation of the simulation process to 

the predicted outcome, and a measure of the confidence level for the predicted 

result. If the time to solution is short, but the quality of and confidence in the 

solution are low, the prediction may be of little value. 

Improved algorithms have resulted in significant performance gains as 

measured by time to solution. Recent studies have noted remarkable progress in 

this area [3, 13, 18]. Figure 5 shows an example of that progress. The figure 

depicts improvements in performance for large-scale simulations of turbulent 

gas-phase combustion [19, p. 79]. As the example shows, advances in simulation 

algorithms have tripled the effective performance over that due to advances in 

processor speed alone over a period of a couple of decades. Similar results have 

been documented in many other domains, such as magnetohydrodynamics and 

radiation transport, and should inspire efforts to obtain and document superMoore’s Law gains in all areas of SBES 

Among the most challenging problems for new algorithms are optimization 

and inverse problems. Simulation-based decision-making gives rise to complex 

optimization problems, which are governed by large-scale simulations. These 

optimization problems appear in engineering design (in which the decision 

variables represent the configuration and constitution of the system) and in 

manufacturing and operations (in which the decision variables represent control 

parameters). Moreover, decision-making informed by predictive simulation 
 51

requires estimations of uncertain parameters that characterize the simulation. The 

response to the resulting inverse problems is to seek estimates for those 

parameters that minimize discrepancies with observations. 

Unfortunately, simulation-based optimization—whether in the form of 

optimal design, optimal control, or inverse problems—is notoriously more 

challenging than the corresponding simulation. First, the optimization problem is 

typically ill-posed, even though the simulation problems themselves are usually 

well-defined. Second, optimization usually results in a four-dimensional spacetime boundary-value problem, despite the evolutionary nature of the forward 

problem. Third, the optimization problem is often formulated in probabilistic 

terms. Fourth, the simulation is merely a subproblem associated with 

optimization, which can be orders of magnitude more computationally 

challenging. Indeed, when the simulation problem requires terascale resources, 

Figure 5. Increases in time to solution due to new algorithms, given in

effective gigaflops over a period of years during which Moore’s Law, the

bottom line on this log-linear plot, remains valid (from [19, p. 79]).
 52

the optimization problem will be in the petascale realm. 

Contemporary optimization methods are inadequate for those tasks. We need 

entirely new classes of scalable, efficient, and robust optimization algorithms that 

are tailored to the complex multiscale, multi-physics simulations engendered by 

SBES. The resulting challenges are of the highest order; yet, they must be 

overcome to fulfill the promise of SBES: to elevate decision-making from a 

practice relying on simple interpolative models to a more rigorous science based 

on high-fidelity predictive simulation. 

 

Finding

Investment in research in the core disciplines of science and engineering at 

the heart of SBES applications should be balanced with investment in the 

development of algorithms and computational procedures for dynamic 

multiscale, multiphysical applications. 
 53

4.0 THE CRISIS OF THE KNOWLEDGE 

EXPLOSION: SBES Education for 

Tomorrow’s Engineers and 

Scientists 

In Volume Two of the SCaLeS Report [19], one finds mention of the “crisis 

of the knowledge explosion.” This expression refers to the dramatic expansion 

of the knowledge base required to advance modern simulation. The expansion 

ignores the traditional boundaries between academic disciplines, which have long 

been compartmentalized in the rigid organizational structures of today’s 

universities. The old silo structure of educational institutions has become an 

antiquated liability. It discourages innovation, limits the critically important 

exchange of knowledge between core disciplines, and discourages the 

interdisciplinary research, study, and interaction critical to advances in SBES. 

The PITAC report [4] lists the following as one its principal 

recommendations [4, p. 9]: “Universities must significantly change their 

organizational structures to promote and reward collaborative research that 

invigorates and advances multidisciplinary science. Universities must implement 

new multidisciplinary programs and organizations that provide rigorous, 

multifaceted education for the growing ranks of computational scientists the 

nation will need to remain at the forefront of scientific discovery.” The report 

goes on to ask: Will research and educational study in the twenty-first century be 

“medieval or modern?” 

The Panel strongly supports the viewpoint of the PITAC Report. If 

simulation is to become a discipline, an engineering tool, and a life-long learning 
 54

opportunity, then the university-level engineering educational system in this 

country must be restructured. The current system does not provide the broad 

range of interdisciplinary knowledge that tomorrow’s engineers and scientists in 

SBES require. To succeed, they must acquire substantial depth in computational 

and applied mathematics, as well as in their specific engineering or scientific 

disciplines. Graduate students, moreover, must be able to build foundations that 

allow them to access quantum and molecular science; statistical and continuum 

mechanics; biological science and chemistry; applied and computational 

mathematics; computer science and scientific computing; and imaging, geometry, 

and visualization. Participation in multidisciplinary research teams and industrial 

internships will give students the broad scientific and technical perspective, as 

well as the communication skills that are necessary for the effective development 

and deployment of SBES. 

The integration of SBES into the educational system will broaden the 

curriculum for undergraduate students. Undergraduates, moreover, will have 

access to educational materials that demonstrate theories and practices that 

complement the traditional experimental and theoretical approaches to 

knowledge acquisition. In addition, SBES will provide a rich new environment 

for undergraduate research, in which students from engineering and science can 

work together on interdisciplinary teams. 

As in any entrenched culture, change is hard to 

come by. To change the culture of separate 

disciplines in U.S. universities will require welldirected, persistent, and innovative federal 

initiatives. The NSF has already done much to 

encourage multidisciplinary research and education 

through initiatives like the ITR and IGERT and DOE 

has the highly successful SciDAC program. Serious 

consideration should be given to turning successful 

NSF will need to 

collaborate with other 

federal agencies to 

open the door for a 

new generation of 

multidisciplinary 

research. 
 55

cross-cutting programs into permanent (but still cross-cutting) administrative 

structures, just as the disciplinary divisions are permanent. 

It is unlikely that the necessary changes in educational structure will come 

without strong directives from leaders from academia, industry, and government 

laboratories. Exactly what changes are needed and how they can best be 

implemented are issues well beyond the scope of this Panel. A detailed study of 

these issues, perhaps undertaken by an NSF-funded committee of the National 

Research Council, could trace out the new educational framework needed for 

effective interdisciplinary study and research. 

The NSF needs to take the lead in “legitimizing” multidisciplinary research. 

One possibility might be the introduction of CAREER awards in 

multidisciplinary research areas. In some areas, NSF will need to collaborate 

with other federal agencies to open the door for a new generation of 

multidisciplinary researchers. A core base of funding should be provided that will 

allow multidisciplinary research and education to flourish. Best practices in 

multidisciplinary education should be identified and then encouraged. NSF 

programs like IGERT or DOE’s Computational Science Graduate Fellowship 

Program provide much-needed funding and guidance for multidisciplinary 

graduate education, but they only have the resources to fund a very few such 

efforts in a given area. More funding is urgently needed for multidisciplinary 

graduate education programs that offer students an integrated approach of team 

research and career development. 
 56

Finding

Meaningful advances in SBES will require dramatic changes in science and 

engineering education. Interdisciplinary education in computational science and 

computing technology must be greatly improved. Interdisciplinary programs in 

computational science must be encouraged, and the traditional boundaries 

between disciplines in higher education must be made pervious to the exchange 

of information between discipline scientists working within multidisciplinary 

research teams. 
 57

5.0 CONCLUSIONS 

This report has documented the findings and recommendations of the Blue 

Ribbon Panel on Simulation-Based Engineering Science, or SBES. As defined in 

this report, SBES is a discipline that focuses on the computer modeling and 

simulation of complex, interrelated engineered systems and on the acquisition of 

data meeting specified standards of precision and reliability. SBES draws on 

advances in scientific understanding and incorporates that understanding into 

new approaches to problems in the engineering domain through computer 

simulation. 

The need for SBES as a distinct field of research comes at a crossroads in our 

nation’s technological development. For almost half a century, developments in 

mathematical modeling, computational algorithms, and the technology of dataintensive computing have led to remarkable improvements in the health, security, 

productivity, quality of life, and competitiveness of the United States. We have 

now arrived at an historic moment. As described in this report, we are on the 

verge of an enormous expansion in our ability to model and simulate an almost 

limitless variety of natural phenomena. That expansion has profound 

implications: 

First, computer modeling and simulation will allow us to explore natural 

events and engineered systems that have long defied analysis, measurement, and 

experimental methodologies. In effect, empirical assumptions will be replaced by 

science-based computational models. 

Second, modeling and simulation will have applications across 

technologies—from microprocessors to the infrastructure of cities. Not the least 

of these new technologies will be effective systems for national security. 
 58

Moreover, new simulation methods will lay the groundwork for entire 

technologies that are only now emerging as possibilities. 

Third, modeling and simulation will enable us to design and manufacture 

materials and products on a more scientific basis with less trial and error and 

shorter design cycles. 

Fourth, modeling and simulation will greatly improve our ability to predict 

outcomes and optimize solutions before committing resources to specific designs 

and decisions. 

Fifth, modeling and simulation will expand our ability to cope with problems 

that have been too complex for traditional methods. Such problems, for example, 

are those involving multiple scales of length and time, multiple physical 

processes, and unknown levels of uncertainties. 

Sixth, modeling and simulation will introduce tools and methods that apply 

across all engineering disciplines—electrical, computer, mechanical, civil, 

chemical, aerospace, nuclear, biomedical, and materials science. For instance, all 

engineering disciplines stand to benefit from advances in optimization, control, 

uncertainty quantification, verification and validation, design decision-making, 

and real-time response. 

There is little wonder that independent studies into the future of the nation’s 

technology are unanimous in their conclusions that computer modeling and 

simulation are the key elements for achieving progress in engineering and 

science. The challenges of making progress, however, are as substantial as the 

benefits. We must, for example, find methods for linking phenomena in systems 

that span large ranges of time and spatial scales. We must be able to describe 

macroscopic events in terms of subscale behaviors. We need better optimization 

procedures for simulating complex systems, procedures that can account for 

uncertainties. We need to build frameworks for validation, verification, and 

uncertainty quantification. Finally, we need methods for rapidly generating highfidelity models of complex geometries and material properties. 
 59

We are not alone in recognizing the urgency of our need to find solutions to 

these problems. Many of our international competitors are well ahead of us in 

committing the necessary funding and intellectual resources to overcome the 

technical problems described in this report. Indeed, the technological superiority 

Americans have so long taken for granted seems to be slipping away. 

To arrest that trend and to help restore the U.S. to its leadership role in this 

strategically critical technology, the Panel has made four recommendations (see 

page xiv of this report for details): 

(1) The Panel recommends that the NSF change its organizational structures 

to facilitate long-range core funding of SBES. 

(2) The Panel recommends a minimum sixfold increase in funding over 2005 

levels of SBES-related disciplines. 

(3) The Panel recommends a long-term program of high-risk research to 

exploit the considerable promise of SBES. 

(4) The Panel recommends that NSF underwrite an effort to explore the 

possibility of initiating a sweeping overhaul of our engineering 

educational system to reflect the multidisciplinary nature of modern 

engineering and to help students acquire the necessary modeling and 

simulation skills. 

These recommendations call for NSF to take decisive and aggressive action 

to support SBES. Unfortunately, over the past decade, NSF and other agencies 

have persistently funded far fewer simulation-related research projects than 

recommended. The difference was sometimes a factor of three and occasionally 

a factor of ten. Moreover, even projects receiving support were frequently underfunded and the grant periods were so short that researchers could only hope to 

achieve incremental advances in the development of key disciplines. By contrast, 

over the same period, funding for SBES research in Europe and Asia increased 

many fold. To overcome these combined shortcomings in funding and duration, 
 60

the Panel recommends at NSF, an annual fund of $300 million be made available 

to advance the SBES components critical to the nation’s security, leadership, and 

competitiveness. 

The Panel recognizes that improvements in the speed and efficiency of 

computers remain important components of advances in SBES. Nevertheless, 

efforts in these improvements should not supersede efforts in other disciplines 

underlying simulation. Instead, NSF should focus on initiatives in SBES that 

promote interaction between multiple disciplines that fit naturally and 

strategically in parallel with or within the Cyberinfrastructure framework. Within 

NSF, SBES should represent a new and fundamental thread of the 

Cyberinfrastructure theme, one that could well call for a parallel program that 

interfaces every division within the Directorate of Engineering, if not across the 

entire Foundation. 
 61

Appendix A: SBES Workshop Attendees

April 2004 Workshop

Workshop Organizers 

 Ted Belytschko (Northwestern) Thomas J. R. Hughes (U Texas-Austin) 

 Jacob Fish (Rensselaer) J. Tinsley Oden (U Texas-Austin) 

Universities 

Narayan Aluru, (U Illinois-UC) Donald Millard (Rensselaer)

William Curtin (Brown U) Robert Moser (U Illinois-UC)

Leszek Demkowicz (U Texas-Austin) Alan Needleman (Brown U)

Charbel Farhat (U Colorado-Boulder) N. Radhakrishnam (N Carolina A&T U) 

Omar Ghattas (Carnegie Mellon) Mark Shephard (Rensselaer)

Anthony Ingraffea (Cornell) Charles Taylor (Stanford)

Chris Johnson (U Utah) Mary Wheeler (U Texas-Austin)

David Keyes (Columbia) 

Government Laboratories 

Thomas Bickel (SNL) Walter Jones (AFOSR) 

John Red-Horse (SNL) Raju Namburu (ARL) 

Roshdy Barsoum (ONR) Noam Bernstein (NRL) 

Luise Couchman (ONR) Jonathan B. Ransom (NASA) 

Craig Hartley (AFOSR) 

 

NSF

 Kamal Abdali (NSF) George Lea (NSF)

 John Brighton (ENG) Priscilla Nelson (NSF)

 Ken Chong (ENG/CMS) Michael Plesniak (ENG/CTS)

 Sangtae Kim (NSF) Galip Ulsoy (ENG) 
 62

September 2005 Workshop

Universities 

 Richard Alkire (U Illinois-UC) Carl Timothy Kelley (NC State) 

 Narayana Aluru (U Illinois-UC) Yannis Kevrekidis (Princeton) 

 Kyle Anderson (UTC SimCenter) David Keyes (Columbia)

 Chandrajit Bajaj (U Texas-Austin) Alan Laub (UCLA)

 Jon Bass (U Texas-Austin) David Levermore (U Maryland-CP)

 Ted Belytschko (Northwestern) Michael Levine (Pittsburgh)

 Larry Biegler (Carnegie Mellon) David Littlefield (U Alabama) 

 Wei Cai (Stanford) Wing Kam Liu (Northwestern)

 Alok Chaturvedi (Purdue) Raghu Marchiraju (Ohio State) 

 Leszek Demkowicz (U Texas-Austin) J. Tinsley Oden (U Texas-Austin) 

 Abhi Deshmukh (U Mass) Steven Parker (U Utah) 

 Craig Douglas (U Kentucky) Linda Petzold (UC-Santa Barbara)

 Charbel Farhat (Stanford) Robert Pennington (U Illinois-UC)

 Jacob Fish (Rensselaer) N. Radhakrishnan (NC A&T U)

 Roger Ghanem (USC) Mark Rashid (UC-Davis) 

 Omar Ghattas (U Texas-Austin) Tom Russell (U Mass) 

 Michael Heath (U Illinois-UC) Mark Shephard (Rensselaer) 

 K. Jimmy Hsiah (U Illinois-UC) David Srolovitz (Princeton)

 Thomas J. R. Hughes (U Texas-Austin) Fanis Strouboulis (Texas A&M)

 Tony Ingraffea (Cornell) Andrew Szeri (UC-Berkeley)

 Chris Johnson (U Utah) Brian Wirth (UC-Berkeley)

 Ramdev Kanapady (U Minnesota) Jacob White (MIT) 

 Sidney Yip (MIT)

DOE, Government Laboratories, and NIH

 Roshdy Barsoum (ONR) Chandrika Kamath (LLNL) 

 Peter Castle (INL) Dimitri Kusnezov (DOE) 

 Luise Couchman (ONR) Habib Najm (SNL)

 Stephen Davis (ARO) Raju Namburu (ARL)

 Evi Dube (LLNL) Tomas Diaz de la Rubia (LLNL) 

 Francois Gygi (LLNL) Daniel White (LLNL)

 Terry Yoo (NLM)

Industry

 Joshua Fujiwara (Honda/OSU) John Ullo (Schlumberger-Doll) 

 Kirk Jordan (IBM) David Young (Boeing)

NSF

 Richard Buckius (ENG/OAD) Wen Masters (MPS/DMS)

 Ken Chong (ENG/CMS) Michael Plesniak (ENG/CTS)

 Frederica Darema (CISE/CNS) M. C. Roco (ENG/OAD)

 George Hazelrigg (ENG/DMI) Celeste Rohlfing (MPS/CHE)

 Deborah Lockhart (MPS/DMS) HenryWarchall (MPS/DMS) 
 63

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