XFEM Simulation on Strong& Weak Arbitrary
Discontinuity
Z. Zhuang, Z. Liu & Q.Zeng
Bridging Scales andMathematical Models witha Generalized Finite
Element Method
C. Armando Duarte
Multiscale Modeling of
Plasticity in Amorphous& Polymeric Materials
S. Li, S. Urata& T. Murashima
Digital & Hybrid Twins:
When Data Meets Physics
F.Chinesta, E. Cueto& J.-L. Duval
Reduced Basis Methods for
Partial Differential
EquationsBook Report by Dan Givoli
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Conference Diary

Bulletin for
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No 46
January 2020

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T

2

XFEM Simulation on Strong & Weak Arbitrary Discontinuity
Zhuo Zhuang, Zhanli Liu & Qinglei Zeng

6

Bridging Scales and Mathematical Models with a Generalized Finite Element Method
Carlos Armando Duarte

10

Multiscale Modeling of Plasticity in Amorphous & Polymeric Materials
Shaofan Li, Shingo Urata & Takahiro Murashima

14

Digital & Hybrid Twins: When Data Meets Physics. A new paradigm for simulation-based engineering
Francisco Chinesta, Elias Cueto & Jean-Louis Duval

20

Reduced Basis Methods for Partial Differential Equations by Alfio Quarteroni, Andrea ManzoniBook Review by Dan Givoli

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Conference Diary Planner

he first issue of IACM Expressions was released on summer
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Long life and best of luck to IACM Expressions !
Eugenio Oñate
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Past President of IACM

XFEM Simulation on Strong &Weak Arbitrary Discontinuity
byZhuo Zhuang,
Zhanli Liu,
Qinglei Zeng
School of Aerospace
Engineering,
Tsinghua University,
Beijing, China
zhuangz@tsinghua.edu.cn

I

ntroduction of strong and weak
discontinuity

The extended finite element method(XFEM) formulation is developed formodeling of strong and weak arbitrary
discontinuity. The conventional strong
discontinuity is a fracture, which uses C-1
function described. An example is plottedin Figure 1 with Mode I and Mode II
hybrid crack propagation at 70oexperimented by Kalthoff (1988) andcrack growth at 72o simulated by ourmodel.

Figure 1:
Kalthoff crack
propagation byexperiment and XFEM
simulation
In contrast, the common weak
discontinuity occurs on the interface ofmaterials, crystal and phase boundaries.
The particle/matrix composite interface at
f=0 is plotted in Figure 2, which is a strain
discontinuity in displacement field used C0
function described.

The level set function is used to evaluate
the location of different phases. The
advantage of XFEM is to adopt regularmesh and no need remeshing during
material deformation, as illustrated in
Figure 3. Another advantage of XFEM
compared with FEM is no very small
element close to interface to match theconfiguration.

Figure 3:
A particle/matrix composite
used by FEM and XFEM mesh,
as well as displacement field
formulaStrong discontinuity ofcontinuum-based shell element

Figure 2:
Particle/matrix composite
with strain discontinuity
at interface

iacm expressions 46/20 2

Distance to interface f(x)f < 0 located in phase A
f = 0 located at interface
f > 0 located in phase B

The shell structure is adopted here to
express strong discontinuity, which has
be widely used in the engineering, suchas aircraft fuselages, ship hulls, pipelines
and so on. Accurate prediction of crack
initiation and propagation path in shell isof great importance to guarantee thesafety and evaluate the failure ofstructure. FEM is a powerful tool toanalyze the safety of shell structure.
However, when coming to crack
propagation problem, the classical FEM
suffers from enormous work in remeshing.

XFEM is introduced into continuum-based(CB) shell element and both jump andcrack tip enrichment functions are
considered [1,2]. This method is improved
to model stationary and propagating crack
in shells [3]. In 1980s, the kinematics
and constitutive equations are derived for“degenerated shell element” from 3D
element based on Mindlin-Reissnerassumptions. The model has been
extended to nonlinear analysis[4].
In this study, a formulation of XFEM on
CB shell element is proposed to model
crack in shell structure, which candeal with geometrical nonlinearity
simultaneously. The CB shell element
is degenerated from 3D solid element.
The crack is not necessarily perpendicular
to the shell middle surface and slanted
crack can be considered in CB shellelement.

Figure 4:
A CB shell element containing a crack front

A quadratic CB shell element is depicted
in Figure 4, which is composed of 8
master nodes and 16 slave nodes,
denoted by filled nodes and empty nodes,
respectively. Every two slave nodes areconnected with one master node by adirector, which can also be called as“fiber”. The nodal coordinates co-rotatewith the fiber. The well-known Euler’s
equations of motion can be solved at
middle surface based on the masternodes.

Figure 5:
XFEM enrichment of CB shell

An example of XFEM simulation on CB
shell is the pressured pipeline with aninclined initial crack penetrated throughthickness to a hoop band, as shown in
Figure 6(a). The crack propagation path isshown in Figure 6(b~d). The crack propagates as a “wing crack” simulated by CB
shell elements independent of the mesh.
The XFEM technology is developed andimplemented in CB shell element to model
arbitrary crack propagation. Specially, thecrack is not necessarily perpendicular to
the middle surface of shell element andgeometrical nonlinearity is considered. It
should be pointed out that this method canbe extended to elastic-plastic problems.

Figure 6:
Shell structure withinclined crack:(a) geometry,(b~d) contour of
displacement field in Z
direction with internalpressure as 0.1MPa,
3.5MPa and 6.0MPa,
respectively

The displacement approximation is basedon the slave nodes. Both slave nodes andmaster nodes are enriched and the enrichment functions are established on slavenodes, as shown in Figure 5. For a CB
shell element containing a crack front, theslave nodes are enriched by crack tip
functions for 3D cracks. For the cracked
elements behand the tip location, aHeaviside step functions are used forenrichment.
3 iacm expressions 46/20

Table 1:
Parameters of matrix

The CB shell element is degenerated
from 3D solid element and so many
features are constructed based on solid
element. For example, enrichment shapefunctions are calculated on solid element
and 3D constitutive law can be used justwith a little modification, as well as thelevel set method is adopted for 3D
crack. The stress intensity factor is also
calculated by equivalent domain integral
method and interaction integral for 3D
crack. However, it is found that theauxiliary fields with the leading terms
for crack in Mindlin-Reissner plate can’tsatisfy exactly the equilibrium equationsor the strain-displacement relationship, a
revised term is retained in this study to
get results independent of integral domain.

The parameters of glass spheres are
R=150µm, E=64.89GPa, ν=0.249,
ρg=2.47g/cm3. The volume fractions are
Vf=8.6% and 45.1%, respectively. The
pressure is p0=10MPa with p=p0sin(2πft).
The parameters of viscoelastic matrix are
listed in Table 1.
The attenuation coefficient α is computedby the equation given below, which indicates the pressure attenuation from input
to output.

Weak discontinuity of polymer
composite under shock wave

Figure 7:
Particle/matrix model
under shock wave

For the weak discontinuity by XFEM,
an example is given to model thesupersonic wave propagation in glass
spheres/epoxy [5,6]. The model geometry
configuration is 1x1x2mm with 40x40x80
brick elements. The shock wave period
is t0=103µs with time increment is∆t=1µs. The glass spheres are randomly
distributed in epoxy matrix by using
random sequential adsorption (RSA)
algorithm. There is no overlap between
any inclusions. The center position andEuler angles obey some distribution, as
shown in Figure 7.

8(a)

8(b)
Figure 8(a) & (b):Compared attenuation
coefficient with published results

In (S1 / S2) / (x1 - x2) nepers/mm,
where Si is a surface of input or output
facing the pressure and x1 - x2 is adistance between the surfaces. For alower volume fraction 8.6%, as shownin Figure 8(a), there is a little effect on
attenuation. While for a higher volume
fraction 45.1%, there is significant effecton attenuation corresponding to wave
frequencies. The simulation results by
XFEM are consistent with the experiment
data in Figure 8(b).
iacm expressions 46/20 4

Figure 9:
Stress distribution infiber/matrix interfacial

propagation. There are six short fibers
embed in matrix, as shown in Figure 9.
The stress distribution is plotted byinterfacial strength of σ=0.2GPa. The
parameters of elastic fiber are E=80GPa,
ν=0.37, ρ=2.47g/cm3, D=100µm, L=10D.
The parameters of viscoelastic matrix are
listed in Table 1.
The shock wave attenuation by interfacial
strength is plotted in Figure 10 as shock
period t=0.8µs. When interfacial stressis very small, such as σ=0.0GPa (redcurve), there are almost 60% pressuresattenuated from input (pink curve) [7]corresponding to shock wave could notpass through the interface.
Figure 10:
Shock wave attenuation
by fiber/matrix interfacial
strength

The second example is a 2×2 mm plate(plane strain) given to evaluate the shock
wave attenuation mechanism atfibers/matrix interface during shock wave

References:
[1]
[2]
[3]
[4]
[5]
[6]
[7]

Zeng QL, Liu ZL, Xu DD, Wang H, Zhuang Z. Modeling arbitrary crack
propagation in coupled shell/solid structures with X-FEM,
Int. J. Numer. Meth. Engng, 2016, 106:1018–1040
Zhuang Z, Cheng BB. A novel enriched CB shell element method for
simulating arbitrary crack growth in pipes, Science China-Physics
Mechanics & Astronomy, 2011, 54: 1520-1531
Zeng QL, Liu ZL, Xu DD, Zhuang Z. Modeling stationary and moving cracks
in shells by X-FEM with CB shell elements, Science China-Technological
Sciences, 2014, 57: 1276-1284.
Hughes TJR, Liu WK. Nonlinear finite element analysis of shells: Part I.
three-dimensional shells, Computer Methods in Applied Mechanics andEngineering, 1981, 26: 331-362
Liu ZL, Oswald J, Belytschko T. XFEM modeling of ultrasonic wave
propagation in polymer matrix particulate/fibrous composites, Wave Motion,
2013, 50: 389-401
Liu ZL, Menouillard T, Belytschko T. An XFEM/Spectral element method for
dynamic crack propagation, International Journal of Fracture, 2011, 169: 183-198
Zhuang Z, Liu ZL, Cheng BB, Liao JH. Extended Finite Element Method,
Elsevier/Tsinghua University Press, 2014
5 iacm expressions 46/20

Bridging Scales and Mathematical
Models with a Generalized
Finite Element MethodT

he Generalized or eXtended FEM [1–7]byhas been developed and improved for
Carlos Armando Duarte
University of Illinois at more than two decades and is available in
Urbana-Champaign, USA several mainstream commercial software
caduarte@illinois.edu such as Abaqus, LS-DYNA, OptiStruct,
ANSYS, etc. The GFEM and XFEM areessentially identical methods: the namegeneralized FEM was adopted by Duarte,
Babuska, and Oden in 1995–1998 and thename extended FEM was coined by
Belytschko and colleagues in 1999–2000 [8].
The acronym GFEM is adopted in this article.
The method removes many of the meshingrequirements of the standard FEM, such as
fitting meshes to crack surfaces or materialinterfaces. The generation of these meshes
is not trivial and often requires user intervention when dealing with 3-D crack propagationor the evolution of material interfaces.
Furthermore, for some classes of problems,such as those governed by the Helmholtz or
Poisson equation, the GFEM can deliveraccurate solutions using meshes that aremuch coarser than those required by the
FEM [9].
The GFEM is an instance of the Partition ofUnity Method [10–13] which is based on two
key ideas: (i) The use of a-priori knowledge
about the solution of a class of problems todefine local approximation spaces; and(ii) The use of a Partition of Unity (PoU) to“paste together” these local approximations.
Several families of functions can be usedas the PoU: Lagrangian finite element
shape functions, meshfree functions suchas Shepard and Moving Least Square,
B-splines, etc. In the case of the GFEM,
the PoU is provided by Finite Element
shape functions α, α = 1,...,nn, where nn is
Figure 1:
Computation of a GFEM
shape function.
Function α is the
FE shape function definedon node α of a FEM mesh.
It provides the PoU for
the GFEM.
Function Eαi isan enrichment function
assigned to node αiacm expressions 46/20 6

the number of nodes of an FE mesh. A
GFEM shape function is given by the productof a basis functions Eαi of a local approximation space and an FE shape function:αi(x) = α(x)Eαi(x). Figure 1 illustrates thecomputation of αi. The basis functions areoften called enrichments. The GFEM has
been popular in solving fracture problems.
Figure 2 shows a GFEM discretization of abranching crack. The discontinuity acrossthe crack surface is approximated by enrichment functions instead of a mesh fitting it.
What if A-Priori Analytical Knowledge
is Limited or Not Available?
Analytical a-priori information is available formany classes of problems, such as Linear
Elastic Fracture Mechanics, cohesive fractures (outside of the fracture process zone),material interfaces, acoustic wave propagation, etc. However, in most cases, the available analytical information is limited to simple
geometries and physics, for example, 2-D
planar cracks with straight fronts. While thislimited information can still be used whensolving problems with more realistic fracture
geometries, mesh refinement is required tocompensate for the suboptimal enrichments,
as illustrated in Figure 2. For some classes
of problems, however, a-priori information
may not exist at all. Most nonlinear and multiscale problems fall into this category. The
required mesh refinement in this case will be
on par with a standard FEM, negating someof the attractive features of the method.
This shortcoming of the GFEM, and in fact of
any PoU method, can be removed by adopting numerically computed enrichments,
instead of relying only on analytical information. Most engineering analyses involve
many time or load steps. The idea is then to
use the numerical solution at a simulation
step to define enrichment functions for the
next step. Thus, the enrichment functions areproduced numerically, on-the-fly, instead ofbeing analytically defined. This allows theuse of coarse meshes while still delivering
accurate approximations and to bring thebenefits of the GFEM to problems that werebeyond the scope of the original method.

Bridging Scales and Mathematical
Models with Global-Local Enrichment
Functions
Numerically defined enrichment functions
are provided by the solution of auxiliaryboundary or initial boundary value problems.
A global-local analysis is then performed,
based on the auxiliary local BVPs and theoriginal global BVP. The GFEM with thisclass of enrichments is denoted as GFEM
with global-local enrichments (GFEMgl) [14,15]. Figure 3 illustrates the application of themethod to the simulation of a propagatingcrack. The solution of the global problem
at a propagation step is used to update thecrack surface and also to provide boundary
conditions for a local problem defined in theneighborhood of the updated crack front.
The local problem is solved using the best
available numerical method for the quantities
of interest. The solution of the local problem,
uL, is then used to enrich the global meshusing GFEM shape functions defined by
αi(x) = α(x)uL(x), where α is an FEM
shape function from the global mesh. Away
from the crack front, analytical information
is often available and thus can be used todefine GFEM shape functions, as shown in
Figure 3. The arrows in the figure show thetwo-way information transfer between the
global and local problems through boundary
conditions and enrichment functions – themethod accounts for the coupling between
the global and local scales. This is incontrast with a Global-Local FEM (GL-FEM)
analysis, which is broadly used to solve
problems with multiple scales of interest [16].
In the GL-FEM, the analysis ends at the local
problem – there is no feedback from the local
to the global scale. This often leads to large
approximation errors, while the GFEMgl candeliver accurate solutions using coarse global
meshes like the one shown in Figure 3.

Figure 2:
GFEM discretization of a
3-D branched crack.
Mesh refinement nearcrack fronts is requiredfor discretization
error control
The GFEMgl offers significant flexibility in theselection of discretization methods for the
global and local problems, and also in the
choice of mathematical models adopted ateach scale. This is important because themost appropriate model for phenomena
at the lower scales of a problem is often
too expensive and, in fact, not needed
everywhere in the analysis domain. As an
example, the simulation of crack branching at
the scale of local problems can be modeledusing peridynamics [17] or a phase fieldmodel [18], since these fracture models canrobustly model branching. The peridynamics
or phase field local solution is then used as
an enrichment for the global problem, which
can adopt a more computationally efficientmodel, such as the classical elasticity
equations. Another example is the useof enrichment functions provided by acomputational vademecum, which ispre-computed off-line based on Proper
Generalized Decompositions [19].

Figure 3:
Solution of a crack
propagation problem
using the GFEM with
global-local enrichments(GFEMgl). These functions
are computed on-the-fly
as the crack propagates.
Brown spheres (yellow
cubes) indicate nodes
enriched with global-local(Heaviside) enrichments
7 iacm expressions 46/20

The GFEMgl solution cycle shown in Figure 3
can be repeated within a time or load step
before further advancing the crack. These
global-local iterations are used to address theeffect of inexact boundary conditions applied
to the local problem [20]. Another strategy toaddress the error in boundary conditions is tosolve the local problem in a larger domain
with a buffer zone while still using the local
solution in the domain shown in Figure 3.
An a-priori error estimate accounting for theeffect of inexact boundary conditions applied
to the local problem and the size of the bufferzone is provided in [20].

Figure 4:
Hat-stiffened panel with spot welds

Figure 5:
GFEMgl applied to a hat-stiffened panel. The enrichment functions areprovided by the solution of local problems with spot welds. Red nodesof the global mesh are enriched with these functions. They add only
three dofs to each node enriched, regardless of the local problem size

Industrial Application: Hat-Stiffened PanelAn example of the application of the GFEMgl
to a representative hypersonic structuralcomponent is presented next. The CAD
model of a hat-stiffened panel is shown in
Figure 4. The four stiffeners are connectediacm expressions 46/20 8

to the panel skin by 168 spot welds. A fully
3-D model of the welds is often needed to
predict damage nucleation and evolution
at or near these connections. However, astandard FEM model of a panel and welds
usually requires several million degrees of
freedom (dofs) [21]. Long duration nonlinear
transient simulations based on such models
are therefore extremely expensive.
Furthermore, the generation of the FEM
model is time-consuming. In the GFEMgl, thespot welds are represented using global-local
enrichments provided by the solution of local
problems like those shown in Figure 5.
The figure also shows the fairly coarse
mesh adopted at the global scale. Only aquarter of the panel is discretized due tosymmetries of geometry, boundary conditions
and material properties. The welds are
ignored at this scale. Thus, the global modelis independent of the number and location of
the spot welds, which is quite attractive when
performing design optimizations. It is also
noted that different element types are usedat global and local problems, which isconvenient when dealing with complex
geometries.
A total of 44 local problems are solved inparallel – one for each spot weld. These
computations scale very well since there isno communication among local problems
during their solution [21, 22]. Their solutions
are then used to enrich nodes of the global
model, as illustrated in Figure 5. This
GFEMgl discretization has about 210,000
dofs. The von Mises stress computed at
the global problem enriched with thesefunctions is shown in Figure 6(a). Localized
stress gradients are well-captured on
the coarse-scale mesh and are in good
agreement with the Direct Finite Element
Analysis results, shown in Figure 6(b).
Further verification of the GFEMgl for thisclass of problems, including time-dependent,
nonlinear, and multi-physics responses canbe found in [21, 23–25].
Transition to National Laboratories
and Industries
A GFEMgl solver can be combined withlegacy FEM software using non-intrusive
algorithms which require no modifications
to the FEM code [26]. This is possible
because the GFEMgl can adopt coarse
meshes of classical finite elements at the
global scale and the local meshes neednot be conforming with the global ones asshown earlier. Non-intrusive algorithms
provide a pragmatic and fast path forthe transition of the GFEMgl to national
laboratories and industries.

Figure 6:
Contour plots of the
von Mises stress on
stiffeners of the
hat-stiffened panel
computed with the
GFEMgl and from a
Direct Finite Element
Analysis (DFEA).
The DFEA model isabout 47 times larger
than the GFEMgl model(a) GFEMgl(b) DFEA

References[1]

T. Strouboulis, I. Babuska, K. Copps, The design and analysis of the generalized finite element method, Computer Methods in
Applied Mechanics and Engineering 81 (1–3) (2000) 43–69.
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Mechanics and Engineering 139 (1996) 289–314.
[3] J. Oden, C. Duarte, O. Zienkiewicz, A new cloud-based hp finite element method, Computer Methods in Applied Mechanics andEngineering 153 (1998) 117–126.
[4] J. Oden, C. Duarte, Chapter: Clouds, Cracks and FEMs, in: B. Reddy (Ed.), Recent Developments in Computational and Applied
Mechanics, International Center for Numerical Methods in Engineering, CIMNE, Barcelona, Spain, 1997, pp. 302– 321,
http://gfem.cee.illinois.edu/papers/jMartin color.pdf.
[5] C. Duarte, I. Babuska, J. Oden, Generalized finite element methods for three dimensional structural mechanics problems,Computers and Structures 77 (2000) 215–232.
[6] T. Belytschko, T. Black, Elastic crack growth in finite elements with minimal remeshing, International Journal for Numerical
Methods in Engineering 45 (1999) 601–620.
[7] J. Dolbow, N. Moes, T. Belytschko, Discontinuous enrichment in finite elements with a partition of unity method,
Finite Elements in Analysis and Design 36 (2000) 235–260.
[8] T. Belytschko, R. Gracie, G. Ventura, A review of extended/generalized finite element methods for material modeling,
Modelling and Simulations in Materials Science and Engineering 17 (2009) 1–24.
[9] J. Melenk, On generalized finite element methods, Ph.D. thesis, The University of Maryland (1995).
[10] I. Babuska, J. Melenk, The partition of unity method, International Journal for Numerical Methods in Engineering 40 (1997) 727–758.
[11] I. Babuska, G. Caloz, J. Osborn, Special finite element methods for a class of second order elliptic problems with roughcoefficients, SIAM Journal on Numerical Analysis 31 (4) (1994) 945–981.
[12] C. Duarte, J. Oden, An hp adaptive method using clouds, Computer Methods in Applied Mechanics and Engineering
139 (1996) 237–262.
[13] C. Duarte, J. Oden, Hp clouds – An hp meshless method, Numerical Methods for Partial Differential Equations 12 (1996) 673–705.
[14] C. Duarte, D.-J. Kim, I. Babuska, A Global-Local Approach for the Construction of Enrichment Functions for the Generalized
FEM and Its Application to Three-Dimensional Cracks, in: V. Leitao, C. Alves, C. Duarte (Eds.), Advances in Meshfree
Techniques, Vol. 5 of Computational Methods in Applied Sciences, Springer, Dordrecht, The Netherlands, 2007, pp. 1–26.
[15] C. Duarte, D.-J. Kim, Analysis and applications of a generalized finite element method with global-local enrichment functions,
Computer Methods in Applied Mechanics and Engineering 197 (6–8) (2008) 487–504.
[16] C. Felippa, Introduction to finite element methods. Course Notes. Department of Aerospace Engineering Sciences,
University of Colorado at Boulder.
[17] M. Schweitzer, S. Wu, Numerical integration of on-the-fly-computed enrichment functions in the PUM,
in: M. Griebel, M. Schweitzer (Eds.), Meshfree Methods for Partial Differential Equations VII, Vol. 100 of Lecture Notes in Computational
Science and Engineering, Springer, Switzerland, 2014, pp. 247–267.
[18] R. Geelen, J. Plews, M. Tupek, J. Dolbow, An extended/generalized phase-field finite element method for crack growth with
global-local enrichment, International Journal for Numerical Methods in Engineering. Submitted.
[19] D. Canales, A. Leygue, F. Chinesta, D. Gonzalez, E. Cueto, E. Feulvarch, J.-M. Bergheau, A. Huerta, Vademecum-based GFEM
(V-GFEM): optimal enrichment for transient problems, Int. Journal for Numerical Methods in Engineering 108 (9) (2016) 971–989.
[20] V. Gupta, D.-J. Kim, C. Duarte, Analysis and improvements of global-local enrichments for the generalized finite element
method, Computer Methods in Applied Mechanics and Engineering 245–246 (2012) 47–62.
[21] H. Li, C. Duarte, A two-scale generalized finite element method for parallel simulations of spot welds in large structures,
Computer Methods in Applied Mechanics and Engineering 337 (2018) 28–65.
[22] D.-J. Kim, C. Duarte, N. Sobh, Parallel simulations of three-dimensional cracks using the generalized finite element method,
Computational Mechanics 47 (3) (2011) 265–282.
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International Journal for Numerical Methods in Engineering 104 (6) (2015) 408–438.
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A nonintrusive approach using a generalized finite element method, Engineering Fracture Mechanics 90 (2012) 41–64.
9 iacm expressions 46/20

M ultiscale M odeling of Plasticity inAmorphous & Polymeric M aterials
by
Shaofan Li
University of California
shaofan@berkeley.edu
Shingo Urata
AGC Inc., Yokohama
Takahiro Murashima
Tohoku University, Sendai

Figure 1:(a) Representative samplingcell (RS-cell) with different
molecular clusters(blue, green, yellow, orange,
red, and silver) in a cubic
specimen of Lennard-Jones
binary glass (LJBG).
RS-cells for (b) amorphoussilicon, (c) LJBG [13] and(d) coarse-grained linear
polymer model [16] (Withkind permission of The
European Physical Journal(EPJ))

iacm expressions 46/20 10Amorphous materials are noncrystalline
solids that include metallic glasses,
glassy polymers, amorphous ceramics,
amorphous thin films, some granular materials, and many biological materials, etc,
which have widely spread applications inmany technology fields. Many of amorphous materials are quasi-brittle and evenductile materials [1, 2] that have high elastic
strength with some to significant ductility,
and the current trend is to understandthe origin of such ductility as well as failure
mechanism in order to control material
responses and enhance their performance.
For instance, metallic glasses can change
their ductility/brittleness dependent on thecompositions, temperature and thermal
history [3, 4]. For amorphous polymers,
their glassy state appears below the glasstransition temperature Tg, and their rubbery
state is above this temperature [5, 6].
Below Tg, the short-range molecularinteractions between nonlinked atoms arestrong, and local loads are carried fromatom to atom. If a small elastic strain isapplied to the polymer, almost all carboncarbon bonds are stretched, and carboncarbon cone angles are distorted. In thecase of large deformation, the atoms (orlarger molecular groups) may flip from one
position to another, leading to nonrecoverable deformations, or plastic deformation.
However, how to quantify such plastic
deformation and its consequences is amajor challenge in computational materials,
because of their complex random
microstructure.
Plasticity in amorphous materials is often
studied by using molecular dynamics (MD)simulations, [7,8,9,10]. On the one hand,
the most accepted phenomenological theory
that describes plasticity in amorphousmaterials is the Shear-transformation-zone
theory (STZ) [8,9,11,12]. However, the STZ
theory is an ad-hoc phenomenologicaldefect-mediated flow theory, and it is not a
microstructure-specific theory, e.g. it does
not take into account the actual disordered
microstructure in different amorphousmaterials, which may result in different
inelastic behaviors. Unlike the dislocationmediate plasticity theory in crystalline solids,
presently we know little about the actual
amorphous material response processduring inelastic deformation, much lessabout their relations to plasticity andductile-to-brittle transition. Moreover,
the STZ theory may not be suitable tolong-chain amorphous polymeric materials.
In this article, we discuss our recent
work that uses a multiscale method tosuccessfully simulate plasticity inamorphous solids at macroscale [13,14,16].
How to define amorphousmicrostructure— Representative sampling cell
Our goal is to develop a macroscale
plasticity theory in amorphous solids basedon microscale rearrangements of atomclusters, defect evolution, and molecular
structure change. Contrary to crystalline
materials, amorphous materials do not haveany definitive microstrucure; however, we
assume that some typical atom or molecule
aggregates may be treated as the representative atomic microstructure for amorphoussolids. To accomplish this goal, we couple
continuum simulation and amorphousmicrostructures through representative sampling cells (RS-cells), which contain multiple
atoms [13,14,16]. The main purpose of the
RS-cell is to intrinsically embed a potential
shear-transformation-zone into macroscalematerial points. For instance, we have built
RS-cells for the Lennard-Jones binary glass(LJBG), amorphous silicon (a-Si) and an
amorphous polymer material models[13,14,16]. In Figure 1 (a), we show a representative sampling cell for LJ-binary system, in which there are several atom clusters that are marked with different colors,i.e. blue, green, orange, yellow, red, and silver. Figure 1(b) to (d) illustrate the RS-cellfor a-Si, LJBG and amorphous polymer
model, respectively.

To representatively describe the material
response, the number of atoms in a RS-cellis crucial, which is dependent on thematerial under studied. For LJBG and a-Si,
64 and 250 atoms are minimum [13], while
polymer and ionic glasses may require
more atoms in order to take into accountthe chain length and the long-range electrostatic interaction. Before defining such
RS-cell, we have conducted a series oflarge scale molecular dynamics simulations,and determined the minimum size of the
RS-cell that can reproduce mechanicalresponses of a larger atomic model withover 30,000 atoms. A systematic study
has been conducted to validate the RS-cell
multiscale approach with the full-atom
molecular dynamics approach for mechanical responses under various applied loading
conditions [13,14].
To reflect statistical variation, a variety of
RS-cells should be prepared. To do so, inthe case of LJBG model with approximately
ten thousands elements, we first construct
an RS-cell database that contains 300
RS-cells, and each of them has 250 atoms
in a distinct statistically representative
configuration [14]. When one starts
macroscale finite element calculation, one
can randomly select one of the RS-cells
from the database as the amorphousmicrostructure for the quadrature point thatis under the consideration.
How to establish an atomistic-informed
constitutive relation?
There have been many multiscale models
developed for crystalline materials e.g. [17,18]. A main technical ingredient of these
multiscale methods is employment of theso-called Cauchy-Born rule (CBR), which
provides a basic rout from microscale tomacroscale. In the multiscale finite element
methods for crystalline materials, the CBR
is applied to an unit cell of a crystal, which
is usually “inserted” into quadrature points
within an element, and we can utilize thisprocedure to obtain macroscale stress at
those quadrature points, e.g. [19].
However, amorphous materials have no
definitive microstructure, one cannot apply
the Cauchy-Born rule to a definite
microstructure, and this is where the current
multiscale method breaks down in the caseof amorphous solids.

atom to anchor the RS-cell, so that wecannot directly apply CBR to derive themacroscale constitutive relation. To
overcome this difficulty, we developed thefollowing coarse-grained Parrinello-Rahman(CG-PR) method, which originates from thewell-known Parrinello-Rahman molecular
dynamics [20,21].
First, we may define an RS-cell shapetensor ash(0) = [a, b, c],(1)where a, b, c are three edge vectors of theunit cell at initial time, as suggested byParrinello and Rahman [20]. Following [20],we may use the cell shape tensor h tore-scales the initial positions of atoms, i.e.
Ri = h(0)Si, i = 1, 2, · · · Nc ,(2)
where Si is the scaled atom positions.

Figure 2:
The multiscale connection
between microscale
shear-transformation-zone
evolution in an RS-cell andthe macroscale stress-strain
relations under pure shear
loading.
(a) Change of relative
positions, local topologicalarrangements, andconfiguration of different
molecular clusters in an
RS-cell corresponding todifferent strain levels:(b) the local region iscentered in a same “blueatom”, and(c) the local region iscentered in a same “yellow
atom” in a real simulation[14]

In macroscale finite element solution, one
can find the deformation gradient F for eachelement, after nodal displacements are
determined for each element. Thus, we canuse the CBR to update current configuration
ri as
ri = FRi = F · h(0) · Si = h(t)Si ,(3)
where h(t) = F · h(0).
Figure 3:
Stress-strain loop under cyclic uniaxial loading at with different strain rates
obtained by using CG-PR method: (a) ε̇ = ± 2.0 × 107s−1, and (b) ε̇ = ± 2.0 × 106s−1(The dotted lines are unloading paths); The stress-strain loop under cyclic shear
deformations at different strain rates: (c) γ̇ = 1.0 × 108s−1, (d) γ̇ = 1.0 × 106s−1,
and the cyclic loading/unloadling hysteresis loops in both uniaxial stretch andshear of the LJ binary glass. (e) Uniaxial stretch: loading rate 2 × 107s−1,
and (f) Simple shear: loading rate 1 × 107s−1 [14]The key step in our approach is that we
employ the RS-cell as the unit cell inamorphous solids to extrapolate stressstrain relation. However, without definite
microstructure, there does not exist a fixed
11 iacm expressions 46/20

with the starting configuration rgi(tn).
Finally we can find the correct 1st
Piola-Kirchhoff stress as follows,(6)
where k is the bond number index.

Figure 4:
The tensile stress versus the tensile strain of uniaxially stretched polymericmaterial obtained from the multiscale simulation (a). The unit of the stress hereis [ c- /σ3 ], c- and σ are the unit of energy and the unit of size of LJ particle in acoarse-grained polymer, respectively. Molecular dynamics simulation results
under different uniaxial engineering strain rates (0.01, 0.015, 0.020 1/τ ) are also
shown in (a). (b) to (f) represent the appearances of the material during
deformation at characteristic in (a). Inset in Figure (a) focuses on the transition
from elastic to plastic deformation. The color shown in (b) to (f) represents themagnitude of the stress at the local finite element; blue is low and red is high.
([16] With kind permission of The European Physical Journal (EPJ))

Figure 5:
Shear band formation in the
single-notched specimen of
Lennard-Jones binary glassmodel by using(a) CG-PR method in
multiscale manner and(b) molecular dynamics simulations at temperature of
0.2 in Lennard-Jones unit.
Right figures in(b) show atomistic view,
while left ones in(b) draw strain distributions

For the CG-PR method, we do not solve
each Si explicitly. On the other hand,
we use multiscale finite element method
to solve F(tn) for each element at time
tn = n∆t. In CG-PR computations, theatom configuration (rgi(tn)) in the current
time step n is guessed from previous
configuration by using the CBR,rgi(tn) = F(tn) · Ri(tn−1) = F(tn) · h(0) · Si(tn−1),(4)where, h(0) is the initial RS-cell shapetensor, Ri(tn−1) is atom coordinate inreferential configuration at time step n - 1,
and Si(tn−1) is normalized coordinates ofatoms in the RS-cell. Once we have initial
guess rgi(tn), the configuration is optimized
to minimize energy by using conjugate
gradient method with periodic boundary
condition in all directions,
riopt(tn) = argminV (ri) ,(5)

The ingenuity of the method is to use theshape tensor h as the motif of the amorphous unit cell, which provides volume
change as well as shear deformation measures. Since we embed the RS-cell at thesite of quadrature points in a macroscale
finite element, when an amorphous material
undergoes deformation, each macroscalematerial point is stressed, and hence eachRS-cell is stressed. When the RS-cell isstressed, the atom clusters inside the RScell will rearrange their relative positions.
To trace microstructure evolution in an
RS-cell, we may divide the atoms in
different regions of the RS-cell into distinct
regions, and make the atoms in theseregions in different colors initially, such asBlue (B), Green (G), Yellow (Y), Organ (O),Red (R), and Silver (S) colors. One canobserve from Figure 2, in which a cubic
specimen is under shear deformation, therelative positions of atoms, cell volumes,
as well as topological connection of theseregions (colored atoms) are completely
different. Indeed, some of the atoms havelarger displacements and thereby create
defects following to the macroscale deformation of the specimen. This clearly showsthat the multiscale method can automatically acquire shear transformation-zonetransition condition, and directly incorporate
it into the macroscale flow stress calculation
while maintaining the elastic stress level
without any empirical inputs.
Multiscale Plasticity
Using the CG-PR method discussed in theabove, here we have simulated macroscale
plastic deformations in LJBG material aswell as an amorphous polymer model.
In addition, we have carried out amacroscale simulation that employs
millions LJBG RS-cells with continuum
finite elements by coupling an open-source
MD code, LAMMPS [22], with in- housefinite element code.
A challenge in amorphous material researchis how to model plastic damage under cyclicloading. Despite of lack of emergenceof defect patterns, plastic damage inamorphous can evolve under cyclic loadingiacm expressions 46/20 12and provide a source of kinematicallyirreversible damage accumulation in
amorphous materials, exhibiting fatigue
failure under cyclic loading conditions well
below the yield stress [23].
Figure 3 shows stress-strain relations under
uniaxial loading and shear deformation, andtheir cyclic deformations were also investigated. If one carefully exams Figure 3 (a)(d), one may find that the stress-strain (S-S)
relations in tension and compression are notcompletely symmetric, and there are noticeable differences in the yield stresses in different directions. In addition, we find thestrain rate dependency of the S-S curves
and existence of hysteresis in stress-strain
relation. Examining variations of the S-S
curves under cyclic loading (see 3(e) and(f)), one may even find the Bauschinger
effect. We have also applied the method tosimulate plasticity in polymetric materials
with long chains. As an example, amaltiscale simulation with an RS-cellcomposed of 100 polymer chains with 100
subsequent beads [15] has conducted,
which uses ten thousands of atoms in an
RS-cell. Such a large model demands

extensive computations; however, a coupled FEM-LAMMPS code isdeveloped to simulate inelastic deformation of polymers up to one
thousand elements, meaning that a multiscale modeling with
100 x 100 x 1000 = 107 atoms is now available [16]. Figure 4(a) shows
the relation between tensile stress and the tensile strain and (b) to (f)display the appearances of the material at the characteristic tensile
strain; 0% (initial state, b), 7% (first yield point, c), 210% (second yield
point, d), 300% (lower yield point, e), 400% (final state, f). The tensilestress increases monotonically from (b) to (c) and from (c) to (d).
The first yield point (c) is the onset of the plastic deformation and thesecond yield point relates to the necking phenomena.
The final example is a simulation of shear band formation in a metallic
glass. Shear banding is a major inelastic failure mechanism of bulk
metallic glasses, and has been studied extensively by experiments e.g.
[24], MD simulations and continuum methods [25]. Following [25], wehave conducted the CG-PR simulation of the same shape specimen(see Figure 5). The specimen with dimension 3mm x 0.1mm x 1mm
is discretized into 9,344 elements, and each element has an RS-cellcomposed of 250 atoms of LJBG model. Consequently, the macroscale
substrate has freedom of 2,336,000 atoms, resulting a large scale
simulation. Figure 5(a) displays shear band formation obtained in a
CG-PR simulation, and the result is visually compared with the MD
simulation result (352,817 atoms) on the same shape model, whose
size is 59.1 x 9.8 x 19.7 nm3 (Figure 5(b)). Further, the simulation
result ensures that a coupling of continuum method and computationallyefficient parallel code of MD simulator allows us to investigate
amorphous materials in a multiscale manner.

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Journal of Chemical Physics, (1990) 92, 5057.
[16] T. Murashima, S. Urata, S. Li, Coupling Finite Element Method with Large Scale Atom- ic/Molecular Massively Parallel
Simulator (LAMMPS) for Hierarchical Multiscale Simulations, Eu- ropean Physical Journal B, (2019) 92, No. 211.
[17] E. B. Tadmor, M. Ortiz, R. Philips, Quasicontinuum analysis of defects in solids, Philosophical Magazine A, (1996) 73, 1529-1563.
[18] A. C. To, S. Li, Perfectly matched multiscale simulations, Physical Review B., (2005) 72, No. 035414.
[19] S. Urata, S. Li, Higher order Cauchy-Born rule based multiscale cohesive zone model and prediction of fracture
toughness of Silicon thin films, International Journal of Fracture, (2017), 203(1), pp.159–181.
[20] M. Parrinello, A. Rahman, Polymorphic transitions in single crystals: A new molecular dynamics method,
Journal of Applied physics, (1981), 52(12), pp. 7182-7190.
[21] S. Li , S. Urata, An atomistic-to-continuum molecular dynamics: Theory, algorithm, and applications, Computer Methods in
Applied Mechanics and Engineering, (2016) 306, pp. 452–475.
[22] S. Plimpton, Fast parallel algorithms for short-range molecular dynamics, Journal of computational physics, (1995) 117(1), 1–19.
[23] C. Deng, C. A.Schuh, Atomistic mechanisms of cyclic hardening in metallic glass,
Applied Physics Letters, (2012) 100(25), 251909.
[24] F. Shimizu, S. Ogata, S.,d J. Li, Theory of shear banding in metallic glasses and molecular dynamics calculations,
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[25] C. H. Rycroft, F. Gibou, Simulations of a stretching bar using a plasticity model from the shear transformation zone theory,
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13 iacm expressions 46/20

D igital & Hybrid Twins:
When D ata Meets Physics
A new paradigm for simulation-based engineeringbyFrancisco Chinesta
Arts et Métiers Instituteof Technology,
Paris France
Francisco.CHINESTA@
ensam.eu
Elias Cueto
Universidad de Zaragoza,
Spain
Jean-Louis Duval
ESI Group, France

I – Simulation-based Engineering
Traditionally, models of materials,
processes, structures and systems are
expressed from partial differential equations involving numerous parameters.
Some of them are related to the physics
itself, others describe the geometry of thedomain in which the problems are defined,
and others are related to the loading andboundary conditions.
Model calibration consists of performing
the needed tests and analysis to extract
all these parameters. It is at this momentthat the problem becomes well posed andits solution is envisaged.
However, the mathematical complexity of
the resulting problems makes its exact
symbolic solution difficult, and numericalsimulation based on discretization
becomes the unavoidable major protagonist. Thus, more than looking for the solution at each point in the domain (there isan infinity) at each time instant (anotherunattainable infinity), we limit our search toa finite number of points (called nodes in
the FEM terminology) and some time
instants, to later evaluate the solution at
any other location and time by simple
interpolation. This constitutes an impressive model reduction, much larger thanother that have become popular recently
and that we will address later.
Discretization leads to linear systemsof equations to be solved, sometimes
several ones, if we address transient“... certification becomescompulsory.
It seems that, now more than ever,verification and validation become
critical ...”
iacm expressions 46/20 14

and/or nonlinear problems. The solutionof those linear systems is easily performedby computers. However, the size of thesesystems (scaling with the number ofnodes) and the number of solutions to beobtained (scaling with the number of time
steps), makes its fast solution difficult. For
this reason, numerical simulation was usually restricted to the design process,where nominal models and loadings (thatare expected to represent well the systemat hand and the conditions in operation)
were solved, leading to nominal designs.
However, at present, with the arrival of
new technologies and practices(autonomous cars, connected systems,
Internet of Things, …) simulation isreclaimed in the production workshop, outside the factory, to accompany components and systems all along their life, in
operation, in service, … In some applications the response and feedback characteristic time becomes not compatible withthe characteristic time of numerical simulation, making the use of standard techniques difficult.
II – Digital twins
The usual simulation-based route that as
discussed consists of using physics-basedmodels, accurately solved by using thejust described discretization techniques
fails when, during operation, the simulation characteristic time is no more compatible with the process characteristic time.
In this case an alternative route exists. It
consists in extracting a model by relating
inputs and outputs, both consisting in
data. These data could be experimentallymeasured or synthetic. In the last case,data comes from models solved by using
the previously referred expensive numerical simulation. From this data, regressionbased metamodels are obtained bymaking use of advanced machine learningtechniques.

Thus, physics-based models will ensure
the offline design and sometimes, moreover, the generation of data that will serve
to create the data-driven models able toproceed online, under the stringent realtime constraints.
During operation, as soon as data iscollected, it can be efficiently assimilated
for making predictions and perform preventive and/or operation maintenance.
A new alliance of physics-based anddata-driven models can be stablishedand efficiently employed in many industrial
applications, with a success that in manycases overpass all the expectations.
III – Beyond digital twins: the hybrid
paradigm
The main issues of the just describedscenario are:
- In the field of engineering, there is an
extraordinary corpus of knowledge,
models, … at the origin of most of theengineering successes of the lastcentury, despite the lack of data;- In engineering, science and technology,
acquiring data is in general expensive(the devices are sometimes extremely
expensive). Performing measurements
involve in many cases technologicaldifficulties. Finally, in other cases, data
is simply unattainable for technicalreasons, or because of regulations ofdifferent nature. Thus, the use of“smart” data seems more valuable than
traditional big-data that applies in otherdomains, but becomes limited in many
areas of engineering, with some
exceptions (e.g. data-series,
imaging, …);

The hybrid paradigm can be expressedas [1]:
Reality = First order physics-basedmodel, manipulated from the tools of
applied mathematics and computerscience + a data-driven correction,
learned on-the-fly, based on data fromthe use of adequate (engineered)artificial intelligence techniques.
The model enrichment is based on theuse of the gap between measurements
and model predictions. The interest ofusing a model is threefold. First, it allows
moving faster on a sound foundation.
Second, the better are the models, thesmaller are the deviations, implying analmost linear or slightly nonlinear behaviorof that deviation, both making possible
their approximation from few data.
This is in sharp contrast to the vast
amount of data needed for creating amodel from scratch. Third, if the design
is certified from the physics-based model,
now the data-driven enrichment acts asa bonus, allowing for better predictions
that the ones obtained from the model
based on the physics that served for
certifying.
Figure 1:(a) parametric aerothermal model in car thermal comfort;(b) parametric crash

(a)

(b)

- Finally, certification becomescompulsory. It seems that, now morethan ever, verification and validation
become critical.
We are proving that by hybridizing
physics-based and data-driven models,
both operating under stringent real-time
constraints, the fulfillment of these threerequirements becomes possible.

15 iacm expressions 46/20

Advanced Model Order Reduction
for efficiently addressing
physics-based models
One may think that the solution ofphysics-based models could beaccomplished with the mere use of more
powerful computers. Even when this is avaluable route, it makes the accessibility
to the appropriate simulation resources
challenging. The same happens for its
integration in deployed systems. Recent
history has proved that this is a criticalfactor for small and medium-sizedcompanies. Thus, in order to democratize
simulation, new solutions are required.
Model Order Reduction (MOR) techniques
open new possibilities for more efficientsimulations [2].
A feasible alternative within the MOR
framework consists in extracting “offline”
the most significant modes involved in acollection of model snapshots, and thenprojecting the solution of “pretty similar”

Figure 2:
An example of physical-virtual world interaction.
A virtual, rubberlike dragon is manipulated by the user

iacm expressions 46/20 16

problems in that “reduced space”.
Consequently, we must solve a discrete
problem of very small size at each
iteration or time step. Thus, MOR-based
discretization techniques provide
significant saving in computing-timeallowing to overcome real-time
constraints . Another MOR-based routeconsists in calculating offline, using all
the needed computational resources(e.g., HPC) and computing time, aparametric solution that contains thesolution for all possible (parametric)
scenarios.
This parametric solution can then beparticularized online to any scenario using
mainstream computational facilities,
including tablets or even smartphones.
With them we will be able to perform
efficient simulation, optimization, inverse
analysis, uncertainty propagation andsimulation-based control all underreal-time constraints . Such a solution
has been demonstrated in many
applications where the Proper Generalized
Decomposition (PGD) method is used [3].

Figure 3:
A hybrid twin for the analysis
and control of a nonlinear
foam beam [12]The main difficulty of applying the standard
PGD constructor just referred is theirintrusivity. For circumventing this difficulty
and making possible the use of standard
solvers for constructing those parametricsolutions (also called virtual chars orvademecums) one can apply robustinterpolation or regression techniques
operating on the snapshots coming fromhigh-fidelity solutions for different valuesof the model parameters. Special caremust be paid for ensuring the scalabilityof those techniques with the number ofparameters and for providing error
estimators/indicators.
A fist proposal, called SSL Sparse
Subspace Learning , allowed theconstruction of parametric solutions when
the number of parameters remainedmoderate [4]. Later, the so-called sparse
PGD (s-PGD) allowed increasing thenumber of parameters, with, in manycases, an almost linear scalability of thenumber of snapshots with respect to thenumber of parameters involved in themodel. Its main ingredients are theseparated representation, the sparsity(inspired of sparse sensing [5]) and theuse of kriging to avoid large oscillations
when constructing interpolations fromarbitrary collocation points [6].
Even if, in many cases, parameters
involved in a model are quite natural,in other cases are much less evident.
For example, when considering physics
depending on parametrized shapes.
In that case, advanced nonlinear dimensionality reduction techniques allow to
extract the minimum number of explicative
uncorrelated parameters, sometime
involving complex combination of them.
We proved in [7,8] that when addressing
the mechanics of organs (e.g. humanlivers) or car aerodynamics (depending on
the cars shape) few parameters suffices,
facilitating the construction of parametricsolutions.
Figure 1 depicts the use of two parametricsolutions, both constructed by using thesPGD.

Engineered Artificial Intelligence
When data is abundant and we proceed
offline, the use of deep-learning is an
excellent candidate. However, whendata is very scarce and the data-driven
model must adapt in time very fast, othertechniques able to operate in thesecircumstances could and should beconsidered, analyzed, developed,
improved and tested on real use cases.
This analysis constitutes the main purposeof the so-called Engineered AI thatembraces: (i) visualizing multidimensional
data; (ii) classifying data; (iii) modelingthe input / output relationship enabling
quantitative predictions; (iv) certifying
those predictions; (v) explaining themthat is, extracting knowledge from theavailable data , and (vi) applying themin service (taking advantage of the online
adaptation or hybrid paradigm) andtraining final users or even students on
the use of AI techniques for adding value
to industrial technology.
Some advanced modelling techniques
concern:(i) sparse-multi-local-multi-parametric nonlinear regressions working at the low-data
limit [6];(ii) the combination of dynamic mode
decomposition (DMD) with reduced modelling leading to the incremental-DMD(iDMD) [9];(iii) techniques able to proceed with heterogenous data (categorial, discrete, qualitative, …) by mapping them from a representation space to a vector space [10];17 iacm expressions 46/20

Figure 4:
Experimental measurements (denoted by “+” symbols),
Euler-Bernoulli-Navier prediction (asterisks) and self-corrected model by
s-PGD methods, denoted by open circles.
The legend represents the absolute error, in mm.
These values can be lowered by improving the resolution of the camera (placing it closer
to the beam, for instance).
(iv) the introduction of physics into thelearning process, specially appealing
when addressing mechanical systems
obeying the principles of thermodynamics[11]; and (v) the use of appropriate metrics(other than the Euclidean) for performing
machine learning, one promising framework being the Topological Data Analysis[11], crucial for describing and parametrizing complex morphologies and topologies.
Virtual, Augmented and Mixed reality
The just mentioned capabilities of hybrid
methodologies pave the way for the development of physics-based virtual, augmented and mixed reality applications. Video
streams run at some 30-60 frames per
second, so it is now possible to develop
simulation apps that, running in the background, obtain predictions for decision
making, assimilate data or even interact
with the physical “reality”. In Figure 2, forinstance, we show an example of virtualphysical worlds interaction. It shows how a(virtual) rubber dragon deforms under avirtual manipulation.
IV – Illustration
To illustrate the capabilities of a hybrid
iacm expressions 46/20 18

twin, consider, for instance a foam beam,shown in Figure 3. We constructed a
hybrid twin of the beam with the help of(i) computer vision for data acquisition(ii) a classical Euler-Bernoulli-Navier(linear) beam model(iii) augmented reality for assisting indecision making processes.
The twin is able to detect systematic
biases among measurements (displacement) and predictions given by the simple
model. By employing sparse-PGD, themodel can be corrected by the twin itself
without any human intervention. This
correction, thanks to the s-PGD
methodology, involves very scarce data
and a minimum number of measurements[12]. In addition, the twin performs data
assimilation in real time. In this case,see Figure 3, it is able to locate theposition of the load (see the red arrow)and correct the assumed model if it
renders systematic errors.
The next natural step is including thehuman in the loop, but for that knowledge
must be approached in a more
systematics and rational way, by certainly
constructing a multi-scale theory of data,a sort of thermodynamics of data,
where entropy will become again amajor protagonist [13].

V – Final remark
As Einstein claimed, it is difficult to change
the paradigm from within. In other words,
imagine the consequences if the nowadays computing revolutions in 40 or 50
years. Gauss claimed that we, now, are asort of clowns for people observing us in
the future. Napoleon invited Alessandro
Volta to the French Academy of Sciences,
to show the actions of the just discovered
electricity (provided by his battery) onsome frogs that jumped by the action of
the applied electrodes. At the end of theexhibition Napoleon asked Volta: Do you
think that the so-called electricity could
serve a day to something other than
inducing jumps to frogs?
We are totally persuaded that concerning
artificial intelligence we are at the stage of
Volta’s frogs. Something incredible andunimaginable for us will come, but thistime it is not necessary to attend one

century. The use of all this power will
depend, once more, on us. Technologies
are not good or bad: it is their use whatmatters, and this is our responsibility.
Acknowledgments:
Authors knowledge the contribution ofmany colleagues:
R. Ibanez, G. Quaranta & N. Hascoet(Arts et Métiers), D. Gonzalez, A. Badias,
B. Moya & I. Alfaro (Zaragoza) andA. de Rouvray, S. Guevelou, F. Daim &A. Chambard (ESI)“The use of all this power will depend,
once more, on us. Technologies are notgood or bad: it is their use what matters,
and this is our responsibility.”

Reference[1]

F. Chinesta, E. Cueto, E. Abisset-Chavanne, J. L. Duval, F. El Khaldi.
Virtual, Digital and Hybrid Twins. A new paradigm in data-based engineeringand engineered data. Archives of Computational Methods in Engineering,in press, 2018.
[2] F. Chinesta, A. Huerta, G. Rozza, K. Willcox. Model Order Reduction.
Encyclopedia of Computational Mechanics Second Edition, Edited by Erwin Stein,
René de Borst and Thomas J.R. Hughes., John Wiley & Sons, Ltd., 2017.
[3] F. Chinesta, A. Leygue, F. Bordeu, J.V. Aguado, E. Cueto, D. Gonzalez, I. Alfaro,
A. Ammar, A. Huerta. PGD-based computational vademecum for efficientdesign, optimization and control. Archives of Computational Methods inEngineering, 20(1), 31-59, 2013.
[4] D. Borzacchiello, J. V. Aguado, F. Chinesta. (2019) Non-intrusive sparse
subspace learning for parametrized problems. Archives of Computational
Methods in Engineering, 26(2), 303-326.
[5] D. L.Donoho. (2006). For most large undetermined systems of linear
equations the minimal 1-norm solution is also the sparsest solution.
Communications of Pure and Applied Mathematics, 50(6): 797-829.
[6] R. Ibañez, E. Abisset-Chavanne, A. Ammar, D. González, E. Cueto, A. Huerta,
J. L. Duval, F. Chinesta. A multi-dimensional data-driven sparse identification
technique: the sparse Proper Generalized Decomposition. Complexity,
2018. Paper 5608286.
[7] D. Gonzalez, E. Cueto, F. Chinesta. Computational patient avatars for surgery
planning. Annals of Biomedical Engineering, 44(1), 35-45. 2016.
[8] A. Badias, S. Curtit, D. Gonzalez, I. Alfaro, F. Chinesta, E. Cueto. An Augmented
Reality platform for interactive aerodynamic design and analysis.
International Journal for Numerical Methods in Engineering, 120:125–138, 2019.
[9] A. Reille, N. Hascoet, Ch. Ghnatios, A. Ammar, E. Cueto, J.-L. Duval, F. Chinesta,
R. Keunings. Incremental Dynamic Mode Decomposition: A reduced-model
learner operating at the low-data limit. In press, CRAS Mécanique.
[10] C. Argerich, R. Ibanez, A. Barasinski, F. Chinesta. Code2Vect: An efficientheterogenous data classifier and nonlinear regression technique.
In press, CRAS Mécanique.
[11] B. Moya, D. Gonzalez, I. Alfaro, F. Chinesta, E. Cueto. Learning slosh dynamics
by means of data. Computational Mechanics, 64, Issue 2, pp 511–52, 2019.
[12] B. Moya, A. Badías, I. Alfaro, F. Chinesta, E. Cueto. Digital twins that learn
and correct themselves. Submitted, 2019.
[13] F. Chinesta, E. Cueto, M. Grmela, B. Moya, M. Pavelka. Learning Physics from
Data: a Thermodynamic Interpretation. https://arxiv.org/abs/1909.01074
19 iacm expressions 46/20

Reduced Basis Methods
for Partial Differential
Equations
Alfio Quarteroni, Andrea Manzoni& Federico Negri
Springer, Switzerland, 2016

B
O
O
K

R
E
V
I
E
W

iacm expressions 46/20 20

ISBN: : 978-3-319-15431-2, 296 pages, soft cover, £52 (List Price)
Contents: Preface; 1: Introduction; 2: Representative Problems: Analysis and (High-Fidelity)
Approximation; 3: RB Methods: Basic Principles, Basic Properties; 4: On the Algebraic andGeometric Structure of RB Methods; 5: The Theoretical Rationale Behind; 6: Construction of RB
Spaces by SVD-POD; 7: Construction of RB Spaces by the Greedy Algorithm; 8: RB Methods in
Action: Setting up the Problem; 9: RB Methods in Action: Computing the Solution; 10: Extension
to Nonaffine Problems; 11: Extension to Nonlinear Problems; 12: Reduction and Control;
Appendix A: Basic Theoretical Tools; References; Index.
This book was published in the Springer series "UNITEXT – La Mathematica per il 3+2", which
is designed for undergraduate and graduate academic courses, and also includes advanced
textbooks at a research level, like this one. The series was originally released in Italian, which
explains its title. The Editor-in-Chief of the series is the well-known computational mathematician
Alfio Quarteroni, who is also the senior author of this book. The other two authors are young
researchers from his former group in EPFL, Lausanne (now Prof. Quarteroni is based at the
Politecnico di Milano).
The subject of the book is Reduced Basis (RB) methods for parametrized PDEs. These methods
constitute a branch within the larger area of Reduced Order Modeling (ROM). The latter has
become a major area of interest in recent years, which many research papers and conference
sessions are dedicated to. The general idea of ROM is to replace a “heavy” numerical model,
called here a “high fidelity” model, with a much more efficient model which retains the same levelof accuracy in some desired sense. This general setup has several variants, one of which is the
subject of this book.
The particular scenario under investigation here is that of a problem governed by a PDE which
involves a set of parameters. One is interested in solving the problem repeatedly for manydifferent values of these parameters. For a very large number of degrees of freedom, say
O(106-109), the computational cost of solving all these many problems, using a standard
numerical method with appropriate resolution, is prohibitive. The book discusses RB methods,
in a variational setting, for solving these problems efficiently, while still obtaining accurate
solutions. The idea underlying Galerkin and Petrov-Galerkin RB methods, on which the bookconcentrates, is the projection of the high-fidelity problem on a subspace spanned by speciallychosen basis functions. The subspace is constructed via a relatively small number ofrepresentative high-fidelity solutions, called "snapshots", for a small number of parameters. The
dimension of the resulting reduced system of equations is related to the number of snapshots.
The book provides the basic mathematical understanding and tools for RB methods. It doesthat very successfully: it is mathematically rigorous but not dry at all, fully covers the theory but
also provides practical algorithms with full details, as well as helpful discussions. The notation
is simple and clear. Still, a list of symbols at the beginning of the book would have been helpful(perhaps in the 2nd edition).
The book is not only well written,
but is also excellently edited,and very pleasant to the eye:definitions, theorems, etc. are
written on a darker background,algorithms are written in boxes,
and there are a decent numberof figures, some of them in color.
Each chapter ends with several
Alfio Quarteroni
Andrea Manzoni
Federico Negri well-designed exercises.

The list of references at the end of the book is comprehensive.
It seems to me that a reader without good background in functional
analysis (despite Appendix A, which serves as a reference to the basic
concepts) and variational formulations will find the book hard to read.
On the other hand, readers with sufficient background in these areas, likemany mathematically-oriented members of the Computational Mechanics
community, will enjoy the book and benefit a lot from it.

Figure 1:
The Introduction provides a number of examples for parametrized PDEs, The snapshots, the manifold and the RB
including interesting problems in optimal control, shape optimization and space. This is Fig. 3.1 in the bookcertain types of inverse problems.
After presenting the problems at thecontinuous level, the authors describe
them at the discrete level, and explain
the need for reduction. Then thebasic theory of RB methods is briefly
outlined. An important distinction ismade here between offline andonline computations.
Figure 2:
Geometry and
The concept of affine parametric dependence is also defined here. This is a situation where theNavier-Stokes
stiffness matrix and load vector depend on the parameters in a "scalar" way. In this case one
streamlines as input ofachieves high efficiency in the assembly of the arrays at the online stage. Most of the bookan advection-diffusion
chapters assume the affine dependence property, but Chapter 10 is dedicated to the extension
problem. This is Fig. 3.2of RB methods to the non-affine case.
in the book
Chapter 2 is a fine treatment of a number of representative problems, which are used todemonstrate the RB methodology throughout the book. All of them are elliptic – time-dependentproblems are not discussed. These include (1) and advection-diffusion-reaction problem,(2) linear isotropic elasticity, (3) Stokes flow, and (4) steady-state Navier-Stokes (NS) equations
for incompressible flow – an example of a nonlinear problem. Section 2.2 provides some tools
for variational formulations. An important classification of problems is made, including strongly
coercive problems, weakly coercive problems (which are inf-sup stable),and saddle-point problems. The latter category is a special case of thesecond category, and as we know from the Babuška-Brezzi theory for
mixed Finite Element (FE) formulations, it is a hard nut to crack. In
Section 2.3 the authors analyze the well-posedness of the three linear
problems defined previously in view of that classification. Section 2.4 isthe analogous discussion at the discrete (algebraic) level. Chapter 2
ends with a short discussion on FE spaces.
Chapter 3 is the heart of the book: here RB methods are developed in theirfull glory, for the general case of inf-sup stable operators. One starts witha set of snapshots for a few parameter sets. Then the RB is constructed byorthonormalization of the snapshots with respect to an appropriate innerproduct. Figure 1, which is Fig. 3.1 from the book, illustrates the process
abstractly. In addition to the Galerkin RB method, the Least Squares RB
method is presented (which can be viewed as a special case of a Petrov-Galerkin RB method).
Here too the methods are discussed first at the continuous level and then at the discrete level.
The efficient implementation of the online process in the case where the discrete operator isaffine is explained, and a posteriori error estimates are given. The chapter ends with a solved
problem of advection-diffusion. Figures 2 and 3 appear in the book in this context.
This chapter includes a few algorithms which are nicely presented. In most of them the authors
indicate, at the side of the algorithm boxes, the complexity (number of operations) associatedwith each of the loops involved, e.g., O(N2). This is a very nice and helpful feature that I havenever seen in any other book before.

Figure 3:
RB solutions of the
problem of Fig. 2,
for different sets ofparameter values.
This is Fig. 3.3 in thebook

In Chapter 4 the authors show the algebraic properties of the various RB methods and theirgeometric interpretation (which "justifies" the use of the terms projection and orthogonality).
Chapter 5 is for the "advanced", and is the most mathematical chapter in the book, although thequestions asked here are quite practical and important: When is a problem reducible? How good
21 iacm expressions 46/20

is the approximation of the solution manifold? How small can the dimension of the RB space be?
Alas, the answers to these questions require heavy mathematics, which culminates in the use of
the Kolmogorov n-width.
Chapter 6 is a very nice treatment of the construction of the RB space via SVD and Proper
Orthogonal Decomposition (POD). POD is a popular way to implement the procedure presented
in Chapter 3 at the discrete level. As an introduction to this topic, the authors explain how tofind, using SVD, the best approximation of a matrix by a matrix of a lower rank (this is the SEY
theorem). They discuss the use of SVD for two applications: image compression and data
mining using the Principal Component Analysis (PCA) approach.
Chapter 7 focuses on the construction of RB spaces using a greedy algorithm, which is different
than POD. An optimization algorithm is called greedy if in each iteration it finds a locally optimal
solution. In the context of RB methods, a greedy algorithm is a procedure which constructs thespace by adding a single vector to the basis in each step. This amounts to searching, in each
step, a snapshot which is the worst approximation of the current RB subspace. The chapter
includes an error analysis of the method.

Figure 4:
Representative RB
solutions for a problem
of a sudden expansion
channel. This is part of
Fig. 11.4 in the book

The RB procedure presented in Chapter 3 requires that the computational domain will not involve
varying parameter values. Chapter 8 shows how to transform a problem in a general domain toa problem associated with a reference domain that does not involve varying parameters. The
procedure is applied to an example of advection-diffusion-reaction. A side comment on thisexample is that some known parameter values are given as numbers (e.g., 100 appearing in theexpression of the thermal conductivity) rather than as symbols. It would have been preferable to
avoid using numerical values in the examples, although I am aware that using only symbols maylead to lengthy expressions.

Chapter 9 contains numerical examples for linear affine problems. Chapter 10 discusses theextension to non-affine problems. The main algorithm to this end is the empirical interpolation
method (EIM). A variant of EIM, called DEIM, where the development is done from the outset atthe discrete level, is also discussed. The comparison between the two is related to the generalquestion of which way to go – apply the computational method at the continuous level andthen discretize, or first discretize and then apply the method. I thinkthat theoretically one should always prefer the former, since it usuallyprovides more mathematical insight. However, sometimes discretizing
from the outset is so much simpler to perform that one is tempted tosacrifice the mathematical insight.
Chapter 11 deals with the major extension of RB methods to nonlinear
problems, and in particular to the NS equations and to a semi-linear elliptic problem. The RB approximation is combined with the Newton methodthat linearizes the problem iteratively. Chapter 12 discusses problems of
constrained optimization and optimal control, and shows how the RB
technique can be used very effectively for such problems. These last two
chapters include impressive numerical examples; see Figures 4 and 5.
In summary, this is an excellent book, highly recommended to readerswith solid mathematical background, on RB methods for an importantclass of problems, namely those governed by parametrized PDEs.
The material covered is very relevant to researchers and graduate
students interested in ROM in general, and is presented in a very effective
manner.
Figure 5:
Solution of an optimal control problem with the initial parameter value(top) and the optimal value (bottom). This is Fig. 12.3 in the bookiacm expressions 46/20 22

The Portuguese Society for Theoretical, Applied and Computational Mechanics
for all inclusions under
APMTAC please contact:
Jose Cesar de Sa
cesarsa@fe.up.pt

C

MN2019 (Congress on Numerical Methods in Engineering) took place in July 2019 at
Guimarães, Portugal. CMN 2019 was a great forum for the discussion of relevantscientific, and technological developments in computational mechanics, numerical methods
and engineering applications.
CMN2019 was jointly organized by the Portuguese (APMTAC) and the Spanish (SEMNI)
Associations and followed the mutual congress editions of Madrid (2002), Lisbon (2004),
Granada (2005), Porto (2007), Barcelona (2009), Coimbra (2011), Bilbao (2013),
Lisbon (2015) and Valencia (2017).
The scientific program comprised of six plenary lectures from internationally known
researchers and was structured in almost 30 thematic sessions of different fields ofresearch. The topics covered ranged from discrete modelling to fluid mechanics,
including multi body dynamics and finite elements, applied to different fields ranging fromcivil engineering to biology. More than 275 participants from 30 countries were present
at CMN 2019, making it a truly international event.
It should be noted that young participants (MSc and PhD students) totalized about 40%of the attendees, demonstrating that the future of this community is promising. Thinking
about the young participants, a unique presentation possibility was offered with oral (moreformal and less interactive) and poster (informal and interactive) presentations of thesame paper. This edition of the CMN also included one day-training courses on different
advanced software packages, which more than 50 participants in total.
Following on the tradition of great social programmes, this edition was no exception, withengaging events in one of the most important historical cities in Portugal, having been
declared World Heritage Site by UNESCO in 2001. During the gala dinner, at a location
with prime panoramic view over the city, the awards ceremony, given by the APMTAC andSEMNI took place, which included Career and Recognition Awards, as well as Young
Researchers and PhD Thesis Awards.
At the end of this year’s event, the next organizing team had a chance to present the venue
for the CMN2021. Grand Canaria was chosen to host the next conference of this series.

Figures 1 & 2:
Overview of the opening ceremony at CMN2019

Figure 3:
APMTAC Board Meeting at CMN2019

Figure 4:
SEMNI Olgierd Zienkiewicz Award to
Prof. Manuel Doblaré
23 iacm expressions 46/20

Tobias Gleim
Stephan Lange

8th GACM Colloquium on Computational Mechanics
28-30 August 2019 in Kassel

Figure 1:
GACM President Michael Kaliske and the
5 plenary speakers Antonio Huerta,
Peter Betsch, Jörg Schröder,Bettina Schröder and Steffen Rothe

Figure 3:
Conference Chairmen
Tobias Gleim (left) & Stephan Lange (right)

Figure 4:
The three GACMBest Ph.D. Awardwinners 2017 & 2018
and the GACM
President (f.l.t.r.
Johanna Waimann,
Silvia Budday,
Michael Kaliske,
Dominik Budday)
Figure 5:
Conference banquet
at Kulturbahnhof
of Kassel
iacm expressions 46/20 24

he 8th GACM Colloquium on Computational Mechanics for
Young Scientists from Academia and Industry was hosted by
the Chair for Mechanics and Dynamics of the department for civiland environmental engineering and by the Chair for Technical andContinuum Mechanics of the department for mechanical engineering of the University of Kassel. Around 180 participants from 14
countries including doctoral students, post-docs, professors andstudents created a lively and friendly atmosphere having many
fantastic talks and even more fruitful discussions. 155 presentations were organized in 21 mini-symposia and held in 33 parallelsessions. The contributions of young researchers were supplemented by four plenary lectures by renowned experts from scienceand industry. Antonio Huerta gave an inspiring lecture on low andhigh-order approximations for computationally-demanding problems, Peter Betsch talked about important topics in the context ofenergy-momentum-entropy schemes, Jörg Schröder explained
crucial phenomena in the characterization of magneto-electriccomposites and Bettina Schröder and Steffen Rothe from
Volkswagen AG gave insights about current challenging research
topics and their implementation. In addition, Silvia Budday,
Johanna Waimann and Dominik Budday, the three GACM Best
Ph.D. Award winners of the years 2017 and 2018 could present
their Ph.D. thesis to the plenary auditorium.
In the poster session, many valuable discussions took place andthe four winners of the GACM Best Poster Award were nominatedin a thorough evaluation procedure by the GACM board and allplenary speakers. During the welcome reception on the firstevening, the GACM Best Poster Awards were announced to theauditorium and handed over to Ivo Steinbrecher, Sonja Uebing,Veronika Straub and Dominik Horny by the GACM President
Michael Kaliske. The valuable mini-symposia were complemented
by social events to strengthen the exchange in additional
discussions in the community by the participants. The banquet
took place at the south wing of the Kulturbahnhof of Kassel, aformer postal center. The culinary meal was served on a former
railway track. Otto Bruhns gave a very entertaining dinner speech
about his time in the 80s at the University of Kassel and its very
interesting peculiarities.

Overall, the 8th GACM Colloquium on Computational Mechanics in Kassel was a great
success and will be succeeded by the 9th Colloquium to be held at the University ofDuisburg-Essen in 2021.

for all inclusions under gacm please contact:
Michael Kaliske
gacm@mailbox.tu-dresden.de

GACM Best PhD Awards
It is a great pleasure to announce that the two outstanding doctoral theses of Dr.-Ing. Johanna Waimann and
Dr.-Ing. Dominik Budday were honored by the GACM Best PhD Award 2018 on the occasion of the 8th GACM
Colloquium on Computational Mechanics in Kassel. Dr. Waimanns’s thesis on “Variationelle Modellierung
irreversibler Effekte in polykristallinen Formgedächtnislegierungen” was supervised by Prof. Hackl at
Ruhr-Universität Bochum. Dr. Budday conducted his work on “High-Dimensional Robotics at the Nanoscale —Kino-Geometric Modeling of Proteins and Molecular Mechanisms” at Friedrich-Alexander-Universität
Erlangen-Nürnberg under the academic supervision of Prof. Leyendecker.
Dr.-Ing. Johanna Waimann: “Variationelle Modellierung irreversibler Effekte inpolykristallinen Formgedächtnislegierungen”
In her thesis, Johanna Waimann deals with variational modeling of irreversible effects
during the solid/solid phase transformation in polycrystalline shape memory alloys. Basedon the principle of the minimum of the dissipation potential, three models are presented toshow the so-called effect of functional fatigue. The first two models couple the phase transformation between the austenitic and the different martensitic variants with plasticity or,
respectively, with the formation of irreversible martensite. Thereby, the simultaneous evolution of the microstructural effects is considered by a coupled dissipation function. To also
examine the influence of thermal cycling, the third material model saves the transformation
history of the body. In her thesis, Johanna Waimann also examined different approaches toaccount for the polycrystalline structure of the material: a combined Voigt/Reuß energy
bound resulting in a grain-wise evolutionof the microstructure and a dynamically
evolving orientation distribution function
for a computationally more efficient
calculation. Several simulations – on
the material point level as well as using
the finite element method – and acomparison to experimental data, proof
the functionality of the models.
Dr.-Ing. Dominik Budday: “High-Dimensional Robotics at the Nanoscale —Kino-Geometric Modeling of Proteins and Molecular Mechanisms”
Proteins are dynamic biomolecules that perform a tremendous variety of cellular functions
on a broad range of spatio-temporal scales, demanding efficient, yet elaborate integrative
computational methods to resolve their motions. In his thesis, Dr. Budday lays out arobotics-inspired, kino-geometric modeling and sampling approach to capture smalland large-scale protein collective motions. He develops a universal theory for geometricrigidity analysis, decomposing macromolecules into larger rigid substructures in a matter of
seconds. The remaining coordinated motions form the basis for a high-dimensional motion
planning algorithm termed dCC-RRT, which integrates the principle of minimal frustration
via dynamic, Clash-avoiding Constraints (dCC) into a rapidly-exploring random tree (RRT).
The algorithm closely approximates conformational transitions and agrees well with motions
from Molecular Dynamics simulations and experimental data. Via large-scale, multi-dataset
benchmark analyses, Dr. Budday further demonstrates how the model captures highly
conserved, protein fold specific, dynamic information that often enters undetected incomparable methods. The developed
tools provide a robust and efficient
alternative to obtain high-level insights
into molecular mechanisms acrossscales, with broad applications inprotein engineering, drug design,and human health.

25 iacm expressions 46/20

5th German-Japanese Workshop on Computational Mechanics
Michael Kaliske
Ines Wollnyn September 23 and 24, 2019, all together 34 Scientists from Japan and Germany
met at Technische Universität Dresden for the 5th German-Japanese Workshop
on Computational Mechanics. The aim of this successful workshop series is to intensify
the scientific relationship between senior and junior German and Japanese researchers
in the broad field of computational mechanics. The workshop was organized by
Prof. Michael Kaliske, head of the Institute for Structural Analysis and current Presidentof the German Association for Computational Mechanics (GACM), in cooperation with
the Japan Society for Computational Engineering and Science (JSCES) and partly
financially supported by TU Dresden and GACM. The previous four workshops in thisseries took place in Sendai, Japan (2017), München, Germany (2015), Yokohama,Japan (2010) and Hannover, Germany (2004).

Figure 1:
Group photo of 5thGerman-JapaneseWorkshop
The scientific program comprised 19 talks on up to date research topics in the wide
field of computational mechanics given by senior and junior scientists from Japan andGermany and fruitful discussions. The program was topped off with a sightseeing walkthrough the old city center of Dresden, the conference banquet, which was held in ahistoric vault of the restaurant “Sophienkeller”, as well as a trip by an old-timer bus to
the winery “Schloss Wackerbarth”. It was a great pleasure to host this workshop andwelcome our friends from Japan in Dresden. We are already looking forward for the
6th German-Japanese Workshop that will be held in Japan in due time.

Figure 2:
Scientific Program

Figure 3:
Sightseeing walk through Dresden
passing Semper Opera House

Figure 2:
At the GAMM-Meeting in Munich 2018

Figure 4:
Workshop Banquet
iacm expressions 46/20 26

Figure 5:
Trip to the winery “Schloss Wackerbarth”

For all inclusions under JACM news please contact:
Hiroshi Okada
hokada@rs.noda.tus.ac.jp

T

he JACM is a union of researchers and engineers working in the field ofcomputational mechanics mainly in Japan. JACM is a loosely coupled umbrellaorganization covering 29 computational mechanics related societies in Japanthrough communication with e-mail and web page (https://ja-cm.org/index-e.html).
The number of individual members is about 350. JACM members actively participate
in the IACM activities.
In 2019, The JACM Computational Mechanics Awards which are the highest awards
were presented to Profs. Hiroshi Okuda (UTokyo), Makoto Yamamoto (Tokyo Univ.
Science) and Makoto Ohsaki (Kyoto Univ.). The JACM Fellows Awards were
presented to Profs. Koji Fukagata (Keio Univ.), Yoshiaki Tamura (Toyo Univ.),
Daisuke Ishihara (Kyushu Inst. Tech.) and Daisuke Tagami (Kyushu Univ.). The
JACM Young Investigator Award were presented to Profs. Akinori Yamanaka (Tokyo
Univ. of Agri. and Tech.), Hikaru Aono (Tokyo Univ. Science) and Shin-ichiro Sugimoto(Hachinohe Inst. Tech.).
The award ceremony washeld in the 2019 JACM
Annual meeting which washeld in Taipei duringd theperiod of APCOM (Asian
Pacific Congress on
Computational Mechanics)
in December 2019.
The details of the 2019
JACM Annual Meeting
will be reported in an
upcomimg issue of
IACM Expressions.

(a)
(b)
(c)
Figure 1:
Computational Mechanics Award: (a) Prof. Hiroshi Okuda (UTokyo), (b) Prof. Makoto
Yamamoto (Tokyo Univ. Science) and (c) Prof. Makoto Ohsaki (Kyoto Univ.)

(a)
(b)
(c)
Figure 2:
Fellows Award: (a) Prof. Koji Fukagata (Keio Univ.), (b) Prof. Yoshiaki Tamura (Toyo Univ.),(c) Prof. Daisuke Ishihara (Kyushu Inst. Tech.) and (d) Prof. Daisuke Tagami (Kyushu Univ.)

(d)

(a)
(b)
(c)
Figure 3:
JACM Award for Young Investigators in Computational Mechanics (a) Prof. Akinori Yamanaka (Tokyo Univ. of Agri.
and Tech.), (b) Prof. Hikaru Aono (Tokyo Univ. Science) and (c) Prof. Shin-ichiro Sugimoto (Hachinohe Inst. Tech.)
27 iacm expressions 46/20

- JSCES Winter School 2019 on Topology Optimizationand its Application for Additive Manufacturing -J

SCES Winter School 2019 on Topology Optimization and its Application for Additive
Manufacturing was held on February 27, 2019, at Morito Memorial Hall of Tokyo
University of Science. This course aims to provide teaching of fundamentals and thetrends of topology optimization with some applications related to additive manufacturing.
In addition, the special lecture was carried out by a researcher on additive manufacturing.
Totally 63 participants consisted of engineers, researchers and university students attended
the course (Figure 1). The lecturers were Prof. Junji Kato (Nagoya University) (Figure 2)
and Assoc. Prof. Kuniharu Ushijima (Tokyo University of Science) (Figure 3).
by: Junji Kato

- JSCES Summer School 2019on Nonlinear Finite Element Method for Elastoplastic Analysis -Athree-day course “JSCES Summer School 2019 on Nonlinear Finite Element Method for
Elastoplastic Analysis” was held during September 10-12, 2019, at Faculty of Science andEngineering (Korakuen Campus), Chuo University (Figure 4). The course has been hosted by
JSCES since 2013, and held consecutively every year (2013 in Tokyo, 2014 in Nagoya, 2015
and 2016 in Tokyo, 2017 in Osaka, 2018 and 2019 in Tokyo), aiming at supporting engineers
and students for their improvement in higher expertise on nonlinear analysis for solids andstructures. The course has been attended by about 60 to over 100 participants every year,
including engineers engaged in a broad range of industries, as well as university and college
students. The total of around 480 attendees have taken the course since 2013.
The topics of the course encompass the basics of constitutive theory and finite element
technology for small and large deformations, as well as practical techniques for numerical
implementation. The lecturers, Profs. Kenjiro Terada, Isao Saiki, Yuki Yamakawa (Tohoku
Univ.), Tateki Ishii (National Institute of Technology, Kisarazu College), Mao Kurumatani(Ibaraki Univ.), and Kazumi Matsui (Yokohama National Univ.) delivered lively lectures(Figure 5). After the lectures, an exchange meeting was held to facilitate communicationand discussion among the participants and lecturers (Figure 6).
by: Yuki Yamakawa

iacm expressions 46/20 28

Daigoro Isobe
isobe@kz.tsukuba.ac.jp- JSCES Summer Camp for Students 2019 -T

he summer camp for students 2019, a successful series hosted by JSCES from2013, was held during September 14-15, 2019. This year, 23 students from various
universities throughout Japan gathered in Nagatoro, which is a scenic town in themountains of Saitama Prefecture (Figure 7). During this camp, 12 students presented
their ongoing researches, and five keynote speakers including a student entrepreneur,
Mr. Ihara (RICOS: Research Institute for Computational Science Co. Ltd), gave talks ontheir research experiences in universities, national laboratories and industry (Figure 8).
The best presentation awards were given to two students (Mr. Minamite and Mr. Kanazawa)from Prof. Yamada, the President of JSCES (Figure 9). Attendees enjoyed a barbequedinner after the lecture meeting and built good relationships through communication in arelaxed atmosphere.
by: Naoto Mitsume- Study Group on Uncertainty Modeling and Simulation -T

his study group was launched in 2015 as one of the study groups in JSCES, aiming
to investigate academic approaches toward uncertainty modeling and simulation.
The group has been actively managed by 25 or more members including Chairperson,
Naoki Takano (Keio University), and Vice-Chairpersons, Tetsuya Matsuda (University ofTsukuba) and two others from the industry. The members are mainly engineers or
researchers in companies such as automotive manufacturers, construction companies
and software vendors, and some members are from governmental research institutes oruniversities.
In the study group, workshops on various topics associated with uncertainty modeling/simulation and V&V had been held several times a year. And this year, seminars open to
the public have been held twice to spread knowledge obtained by the group’s activities(Figure 10). Moreover, based on such activities, we have been organizing a minisymposiumin the annual conference of JSCES every year. Also, we have already organized someminisymposia at international conferences such as WCCM2016 in Seoul and
COMPSAFE2017 in Chengdu, and willorganize minisymposia at the upcoming
COMPSAFE2020 in Kobe and WCCM2020 in Paris. Currently, the members
continue to work actively especially inindustrial-academic collaborative working
groups formed in the study group in 2019,
which are specialized in uncertainty modeling/simulation methods for quenching ofsteel, 3D additive manufacturing, and thestochastic finite element method.
by: Tetsuya Matsuda & Naoki Takano
29 iacm expressions 46/20

for all inclusions under GIMC please contact
Sonia Marfia
sonia.marfia@uniroma3.it
Gruppo Italiano di
Meccanica Computazionale

MINISYMPOSIUM GIMC (Italian Group of Computational Mechanics)
At the AIMETA Congress, Rome 15-19 September 2019T

he AIMETA Computational Mechanics Group (GIMC) is pleased to announce thatthe minisymposium GIMC, held during the AIMETA Congress, in Rome on
September 15-19 2019, was very successful and had a large turnout:
30 presentations and more than 70 participants, from Italian and foreign
Universities;
A special session devoted to the presentation of the PhD theses in
Computational Mechanics winner of the GIMC award;
A special session devoted to young researchers in which the best presentation
has been awarded.

GIMC Best PhD Thesis Award
The GIMC Best PhD Thesis Awards committee, composed by Loredana Contrafatto(Università di Catania), Massimiliano Cremonesi (Politecnico di Milano), andGiovanni Formica (Università di Roma Tre), selected the following thesis defended
during the 2018 year:
Dr. Domenico Magisano, Università della Calabria, “Solid-shell finite element
and isogeometric models for nonlinear analysis and design of elastic shells
using Newton, Koiter and Koiter-Newton solution strategies” for theComputational Solid Mechanics Award
Dr. Elena Gaburro, Università di Trento, “Well balanced Arbitrary-LagrangianEulerian Finite Volume schemes on moving nonconforming meshes for
non-conservative Hyperbolic systems” for the Computational Fluid Mechanics
Award.
GIMC Best Presentation Award reserved to Young Researchers
The committee, composed by Elena Benvenuti (Università di Ferrara),
Stefano de Miranda (Università di Bologna), Giovanni Garcea (Università della
Calabria), Sonia Marfia (Università di Roma Tre), Anna Pandolfi (Politecnico di
Milano) and Giulio Ventura (Politecnico di Torino), awarded the following young
researchers for the best presentation:
Dr. Paolo Di Re, Università della Calabria, “Enhanced beam formulations withcross-section warping under large displacements”
Dr. Francesca Fantoni, Università di Brescia, “Predicting the constitutive
response of heterogeneous materials via machine learning”.
iacm expressions 46/20 30

for all inclusions under KSCM
please contact
Ikjin Lee이익진 ikjin.lee@kaist.ac.kr

KSCM Spring Workshop& Award Ceremony 2019T

he Korean Society for Computational Mechanics (KSCM) Spring workshop
was held in the Coral room at the Millennium Seoul Hilton Hotel on 9 May 2019.
Professor Soo-Won Chae at Korea University was awarded the KSCM medal, andProfessors Maenghyo Cho at Seoul National University and Byungsoo Kim at
Choongnam National University were awarded the KSCM research excellence
awards.
Figure 1 (a, b & c):
Invited lectures by KSCM awardees

1(a)

1(b)

After the award ceremony, they delivered invited lectures. The KSCM medal
and awards are given by the KSCM to individual researchers in recognition of
distinguished contributions to computational mechanics.

1(c)Besides, Professors Sung Youb Kim at UNIST, Heung Soo Kim at Dongguk
University, and Chohong Min at Ewha Woman’s University received the Young
Scholar Awards.
Further information will be found at: http://www.kscm-society.org/.

Figure 2:
Group photo of awardees
and some participants.

31 iacm expressions 46/20

During the past year the Israel Association for Computational Methods in
Mechanics (IACMM) held two symposia. The 45th Israel Symposium on
Computational Mechanics (ISCM-45) was held in Oct 2018 at the Technion - Israel
Institute of Technology, organized by Profs. Pinhas Bar-Yoseph and Sefi Givli.
The interesting opening lecture was given by the international invited speaker Prof.
John E. Dolbow from Duke University, USA, titled “Recent Advances in
Computational Fracture Mechanics and Gradient-Based Damage Methods”.
Figure 1 shows Prof. Dolbow lecturing. Prof. Adrian Lew from the Dept. of
Mechanical Engineering, Stanford University, USA, gave a keynote lecture named“High-fidelity methods for brittle fracture propagation”. The symposium included
10 other lectures, presented by practitioners and researchers from industry andacademia. These included among others talks by Prof. Konstantin Volokh (“Material
sink approach to fracture modeling”), and a tutorial on “Microstructural-basedmodelling of mechanical properties – Studying mechanical properties from theatomic scale”, by Prof. Dan Mordechai, both from the Technion. Mr. Roman Kositski,a PhD student of Prof. Dan Mordehai from the Faculty of Mechanical Engineering,Technion was the winner of the ISCM-45 competition for the best presentation – thetitle of his talk was “The origin of the Hugoniot elastic limit spike and precursor decay
in shocked body-centered cubic metals”. Figure 3 shows Mr. Kositski during his talk,and Figure 4 is a group photo of the Invited speaker and the keynote speaker with
the IACMM executive council.

Figure 1:
Prof. Dolbow delivering the invitedlecture of the ISCM-45,
Oct 2018, Technion, Haifa

Figure 2:
Prof. Adrian Lew during his talkat ISCM-45

Figure 3:
Mr. Kositski, the winner of the
ISCM-45 competition for the best
presentation, during his talkFigure 4:Group photo of IACMM executive council, international speaker, keynote speaker. organizers and tutorial speaker at
ISCM-45, at the Technion. Left to Right: Z. Yosibash, S. Givli, S. Krylov, M. Engelman, A. Lew, J. Dolbow,
A. Herszage, D. Givoli, I. Harari, J. Tal , D. Mordechai and P. Bar-Yoseph
iacm expressions 46/20 32

for all inclusions under IACMM
please contact
Zohar Yosibash
yosibash@tauex.tau.ac.il

Figure 5:
Prof. Wohlmuth delivers the invitedlecture of the ISCM-46, March
2019, Tel-Aviv University

Figure 6:
Prof. Gyries delivers the tutorial
lecture of the ISCM-46, March
2019, Tel-Aviv University

Figure 7:
Dr. Papadimitropoulos, the winnerof the ISCM-46 competition for thebest presentation, during his talkT

he 46th IACMM Symposium was held in March 2019 at Tel Aviv University,organized by Profs. Slava Krylov and Isaac Harari. The delightful opening lecture
was given by the international invited speaker Prof. Barbara Wohlmuth, from theTechnical University of Munich, Germany, titled “Challenges and opportunities fornumerical analysis in simulation technology”. Figure 5 shows Prof. Wohlmuth
lecturing. Seven additional presentations were given at ISCM-46, followed by atutorial on “Deep learning basis” by Prof. Raja Gyries from Tel Aviv University(Figure 6 shows Prof. Gyries during his tutorial). The presentation by Dr. Symeon
Papadimitropoulos, a PD working with Prof. Givoli at the Technion, was elected as
the best presentation at ISCM-46, titled " Imaging in three-dimensional waveguides
with partial aperture data". Figure 7 shows Dr. Papadimitropoulos during his talk and
Figure 8 is a group photo of the Invited speaker with the IACMM executive council at
the festive dinner held following ISCM-46.
The first prize winner of the best presentation at ISCMs in 2018, Mr. Roman Kositski,
and the second prize winner, Dr Yaniv Brick, received an IACMM certificate awardedby IACMM president Prof. Zohar Yosibash during the assembly meeting of IACMM
held at ISCM-46 (Figure 9).
Figure 8:Group photo of IACMM executive council, the international speaker, andtutorial presenter at ISCM-46, at the festive dinner in Tel-Aviv Left to Right:
R. Gyries, S. Krylov, Z. Yosibash, D. Givoli, I. Harari, B. Wohlmuth,
P. Bar-Yoseph and M. Engelman

Figure 9:
Mr. Kositski receiving thecertificate on best presentations in2018 from Prof. Yosibash(president of IACMM)
during the General Assemblyof the IACMM at ISCM-46

33 iacm expressions 46/20

26th UAKCM Conference prizes
The 26th Conference of the UK Association for Computational Mechanics took place from the10th to 12th of April in City, University of London. The conference was organised by Professor
Roger Crouch, Dean of the School of Mathematics, Computer Science and Engineering.
Figure 1:
Group photo of
the 26th UAKCM
conference participantsat the Goldsmiths’ Hallin London

After a long deliberation, the conference organizers awarded three prizes:•
Yuxuan Chen from Cambridge University received the Mike Crisfield Prize for the paper“A Multigrid Approach that is Robust with respect to Mesh Quality”.
•
Andrei Shvarts from the University of Glasgow received the prize to the best
presentation by a post-doctoral researcher for the paper “FEM implementation forNavier-Stokes equations for an incompressible steady flow using hierarchical basis
functions”.
•
Zhi Li from Swansea University
received the prize to the best
presentation by a PhD student for thepaper “An unsymmetric solid-shellelement for geometric nonlinear
analysis”.
From UKACM we want tocongratulate all the winners!

Computational modelling of Complex Materials
across the Scales (CMCS)
The international ECCOMAS Thematic Conference “Computational Modelling of Complex
Materials across the Scales (CMCS)” took place on 1-4 October 2019 in Glasgow.
The objective of CMCS was to elucidate cutting-edge developments in multi-scalecomputational modelling of complex materials, possessing distinct fine-scale structure and/or
exhibiting coupled phenomena. Particular emphasis was on emergent coarse-scale
behaviour due to the underlying fine-scale structure. CMCS thus focuses on both the(experimentally informed) modelling of complex fine-scale structural phenomena, and on
their upscaling to coarser scales. CMCS gathered scientists from different disciplines working
on scale-bridging challenges in complex materials to advance the field significantly.
The conference was chaired by Paul Steinmann (University of Glasgow, UK / Universityof Erlangen-Nuremberg, Germany), Andrew McBride (University of Glasgow, UK),
Marc Geers (Eindhoven University of Technology, The Netherlands) and Julien Yvonnet(Université Paris-Est, France).

iacm expressions 46/20 34

for all inclusions under UKACM please contact
Rubén Sevillar.sevilla@swansea.ac.uk
27th Conference of the UK Association
for Computational Mechanics
The 27th UKACM Conference will take place on April 1-3, 2020 in Loughborough University.
The conference will be organised by Dr Marco Discacciati, Lecturer at the Department ofMathematical Sciences and member of the UKACM Executive Committee.
As it is traditional in UKACM
conferences, there will be a
pre-conference School. This year
the School will focus on virtual
element methods, domain
decomposition methods andreduced order modelling.
The conference will start on
Thursday 2nd April with threerenowned speakers.
More details will be announced
shortly in our website
www.ukacm.org.

2020 International Conference of theEngineering Mechanics Institute
The ASCE engineering mechanics institute international conference will take place on
April 5-8, 2020. The conference is co-organized by Durham and Newcastle Universities
and it is sponsored by UKACM. It will be hosted by Durham University, chaired byProfessor John Brigham.
Submission of abstracts is already open and a number of minisymposia accepted in
topics such as:
Bio-Mechanics/Materials, Computational Mechanics, Uncertainty Quantification andData-Driven Science and Engineering, Design and Analysis of Next Generation
Engineered Materials, Mechanics of Granular Materials and Coupled Systems, Mechanicsof Materials, Mechanics of Structural Components and Systems and Structural Health
Monitoring, Sustainability and Resiliency.

35 iacm expressions 46/20

14 th Conference of theFrench Structural Mechanics Association(CSMA),
13-17 May 2019, Giens, FranceT

he 14th Conference of the French Structural Mechanics Association (CSMA) was
held in Giens, in the south of France by 13-17 May 2019. CSMA conference is held
every two years and is the major event for the French solids computational mechanics
community. The 14th edition gathered 344 participants, 197 oral presentations, 28 posters
and 5 parallel sessions. Distinguished plenary speakers included B. Sudret (ETH Zürich,
Switzerland), A. Pandolfi (Politecnico di Milano, Italy), B. Leblé (Naval Group company,
France), J.J. Marigo (Ecole Polytechnique, France), B. Cochelin (Ecole Centrale Marseille,
France) and Yvon Maday (Sorbonne Université, France).
Events included a roundtable on data sciences in mechanics, a software session and theaward ceremony for the best PhD theses in France on computational mechanics in 2018and 2019. The topical sessions included:
•
•
•
•
•
•
•
•

Statics and dynamics of structures
Numerical methods
Models and behavior of materials
Durability of structures
Biomechanics and bio engineering
Manufacturing processes
Optimization
Contact mechanics, interfaces

In addition, mini symposia have beenorganized on the following topics:
•
•
•
•
•

Data driven computational
mechanics
Digital materials and multiscalemodeling
Additive manufacturing simulations
Bridging experiments andsimulations
Discrete elements, granularmaterials.

More information about the congress can be found on the website
https://csma2019.sciencesconf.org/?forward-action=index&forwardcontroller=index&lang=en
Julien Yvonnet
President of the Scientific committee
Hamid Zahrouni
Chair
iacm expressions 46/20 36

for all inclusions under CSMA
please contact
CHINESTA Francisco
Francisco.CHINESTA@ensam.eu

3 rd CSMA Juniors WorkshopC

SMA Juniors, the section of young researchers (<40) of theFrench Computational Structural Mechanics Association(CSMA), organized last spring the 3rd edition of its annual
workshop. It took place in the beautiful Porquerolles Island(French Riviera) on 11-13 May 2019, and was connected with the14th National CSMA Congress organized nearby in the Giens
peninsula from May 13, 2019. It gathered about 60 participants(PhD students, post-docs, as well as junior researchers withpermanent position), and it was a great success.
As usual, the workshop was a privileged place for scientific exchanges between the young
generation of CSMA, with several social events. Several useful and unique activities withunconventional formats were also proposed such as:a set of short courses (by and for young scientists) on the topics of the CSMA
Congress mini-symposia, including both theoretical and practical parts withnumerical implementation;a brainstorming session on data science, with a preliminary course on the topic;
a session with short (180 s) and theatrical talks;a coding competition.
The 2019 CSMA Juniors Workshop was sponsored by CSMA, Elsevier, and EDF(French electricity company). The workshop organizers warmly thank these sponsors
for their support.
For more information on activities of the CSMA Juniors section, which constitutean evidence of the dynamism of the CSMA association, please visit:
http://csma.asso.univ-lorraine.fr/csma-juniors/
Ludovic CHAMOIN
President of the CSMA Juniors Committee

37 iacm expressions 46/20

USACM Thematic Conference:
Modeling and Simulation of
the Mechanics of Nanoscale Materials

T

he USACM Thematic Conference on Recent Advances in the Modeling and
Simulation of the Mechanics of Nanoscale Materials, was held August 21-23, 2019 in
the Singh Center for Nanotechnology of the University of Pennsylvania and sponsoredby the USACM Thrust Area in Nanotechnology and Lower Scale Phenomena. The Penn
Institute for Computational Sciences and National Science Foundation also provided
support. There were 31 invited speakers, and a total of 64 participants. 20 posters were
presented by graduate student and postdoctoral attendees. The successful workshop
was organized by Celia Reina and David Srolovitz from the University of Pennsylvania,
Long-Qing Chen from Pennsylvania State University, and Vasily Bulatov from Lawrence
Livermore National Laboratory.
The workshop gathered experts at the forefront of the modeling and simulation of
the mechanics of nanoscale materials, while ensuring a diversity of computational
techniques and participants. Approaches discussed included (but were not be limitedto) electronic structure calculations, atomistic modeling, continuum approaches for
microstructure evolution and bridging techniques. These technique oriented presentations were complemented by a diverse set of applications. Session were held on First
Principle Calculations and Quantum Properties, Plasticity, Defects, Grain Boundaries
and Interfaces, Mechanics of Materials, Phase Field Models, and Scale Bridging.
The speakers at the workshop were Vikram Gavini, Amartya Banerjee, Mauricio Ponga,
David Rodney, Eugen Rabkin, John Bassani, David McDowell, Michael Ortiz, EmmanuelClouet, Jaime Marian, Anter El-Azab, Yash Kulkarni, Nikhil Admal, Brandon Runnels,
Garritt Tucker, Tim Rupert, Jun Lou, Mitra Taheri, Ryan Elliot,Prashant Purohit, Andrej
Kosmrij, Pradeep Sharma, Kaushik Dayal, Pedro Ponte-Castanada, Peter Voorhees,
Katsuyo Thornton, Martin Diehl, Jarek Knap, Yuri Mishin, Xin Yan, Dan Mordehai,
and Andrea Liu. Further details about
the conference program can be foundin the conference webpage http://nanomaterials2019.usacm.org
An active student poster session washeld on the first evening of the workshop and was attended by all workshop participants. There were a totalof 20 posters presented.

Figure 1:
Attendees at Recent Advances in
the Modeling and Simulation of
Nanoscale Materials, Philadelphia,
PA, August 20-22, 2019

USACM Upcoming Events
further details at usacm.org
Workshop on Experimental and Computational Fracture Mechanics: Validating Peridynamics andPhase Field Models, February 26-28, 2020; Baton Rouge, Louisiana; http://wfm2020.usacm.org/
Meshfree and Novel Finite Elements with Applications, September 27-29, 2020; Berkeley, California;
http://mfem2020.usacm.org
Isogeometric Analysis 2020, October 18-21, 2010; Banff, Canada; http://iga2020.usacm.org
iacm expressions 46/20 38

for all inclusions under USACM please contact
info@usacm.org

15th U.S. National Congress on
Computational MechanicsT

he 15th U.S. National Congress on Computational Mechanics (USNCCM15)
was held July 28 - August 1, 2019 at the JW Marriott in Austin Texas. Led byco-chairs John Foster, Tan Bui-Thanh and the local organizing committee from theOden Institute for Computational Sciences and Engineering at the University ofTexas, the congress had several highlights, beginning with Texas singer-songwriter
Gary P. Nunn entertaining attendees at the Opening Reception. The congress was
also pleased to honor Professor Mary F. Wheeler for her long and distinguishedcareer in subsurface modeling. Three minisymposia were dedicated to Professor
Wheeler and she was a guest speaker at the Women’s Networking Event.
During the opening ceremony, the 2019 USACM Awards were presented as follows:
1.
2.
3.
4.
5.
6.
a.
b.
c.

John Von Neumann Medal: Michael Ortiz, California Institute of Technology
The Belytschko Medal: Romesh Batra, Virginia Institute of Technology
Thomas J.R. Hughes Medal: John Shadid, Sandia National Laboratories
and the University of New Mexico
J. Tinsley Oden Medal: Suvranu De, Rensselaer
Polytechnic InstituteGallagher Young Investigator Medal:
Ming-Chen Hsu, Iowa State University
USACM Fellows:
Krishna Garikipati, University of Michigan
Lori Graham-Brady, Johns Hopkins University
Yongjie Jessica Zhang, Carnegie Mellon University

Plenary speakers for the congress were Karen WIllcox,
Oden Institute, UT-Austin, Carlos Armando Duarte,
University of Illinois at Urbana-Champaign, Mary F. Wheeler, Oden Institute,
UT-Austin and Travis Oliphant, founder and head of Quansight.

Figure 2:
Professor Mary Wheeler
addressing the participantsof the Women's
Networking Event

Figure 3:
USACM President,
John Dolbow (center) and2019 USACM Awardees

Semi-Plenary speakers were Stewart Silling, Sandia National Laboratories,
Victor Calos, Curtin University; Caglar Oskay, Vanderbilt University; Ellad Tadmor,
University of Minnesota. A new feature of the US National Congress was theSpecial Young Investigator Semi-Plenary Speakers: Miguel Bessa, Delft University
and Michael Tupak, Sandia National Laboratories.
The Plenary and Semi-Plenary talks were recorded and are to be available forviewing at:https://www.youtube.com/channel/UCNZ0Lojf6ww8cKspMGdT47g/.
A student poster competition was held during the afternoon session of the secondday. Six winners were announced at the congress banquet as follows:...
•
•
•
•
•
•

Angran Li, Carnegie Mellon University
Jonathan Russ, Columbia University
Nicole Nellesen, RWTH Aachen
Berkin Dortdivanlioglu, Stanford University
Farzaneh Mousavi, University of Nebraska-Lincoln
Hugh Carson, MIT

Figure 4:
Poster session at
USNCCM15

Over 1000 were in attendance at the congress,
with 75 participating in 5 pre-congress short courses.
The congress also featured the Women's Networking
Event, held in support of and for mentoring women in
research. The next congress will be held July 25-29,
2021 in Chicago, Illinois.

39 iacm expressions 46/20

SEMNI and APMTAC
met this Julyfor their biannual conferenceT

he Spanish and Portuguese associations, SEMNI and APMTAC, celebrate
this July their biannual conference in Guimarães, Portugal. All the relevant
information, along with the program of the congress, is available at
http://www.cmn2019.pt.
Figure 1:
A picture of the
opening sessionof the conference

Nearly 100 Spanish researchers joined the Portuguese community in Guimarães.
The conference was a great success both from the scientific as well as social
points of view.
Figure 3:
Profs. Paulo Lourenço,
Glaucio H. Paulino andOscar Lopez-Pamies
during the plenary lecture ofProf. Paulino

iacm expressions 46/20 40

for all inclusions under SEMNI please contact
Elias Cueto
ecueto@unizar.es

SEMNI awards
2019

S

EMNI has awarded its annual prizes during its last meeting of the executive
board, held in Barcelona the last March 29th, 2019. The awardees will
receive their prizes during the gala dinner ceremony of the joint SpanishPortuguese Conference on Numerical Methods, CMN, to be held in Guimarães,Portugal the next July 1-3rd, 2019.
The list of awardees is the following:
SEMNI award to the best Ph.D. thesis in the field of numerical methods:
The committee, formed by profs. I. Romero (president), M. Arroyo, S. Badía,
L. Cueto-Felgueroso and E. Reina, granted the prize to:• Dr. Aranu Pont, for the thesis "Numerical Simulation of Aeroacoustics
using the Variational Multiscale Method. Application to the problem of
human phonation” advised by Ramon Codina and Joan Baiges (Universitat
Politècnica de Catalunya). This thesis has represented SEMNI for the
ECCOMAS award 2019.
• Dr. Miguel Herráez (ex aequo), for the thesis “Computational
Micromechanics Models for Damage and Fracture of Fiber-Reinforced
Polymers” advised by Carlos González and Claudio Lopes (Universidad
Politécnica de Madrid).
Juan C. Simó award 2019:
SEMNI’s prize to young researchers commemorates the legacy of the late prof.
Juan C. Simó in the field of numerical methods. The committee was formed inthis occasion by profs. P. Díez (president), M. Doblaré, S. Idelsohn, D. Peric and
J. P. Moitinho de Almeida. In this occasion, the awardee is:• Prof. José Reinoso, from the University of Seville. The committee
highlighted his contributions to the field of non-linear solid mechanics,
particularly in composite material modeling and simulation.

Figure 3:
Prof. Doblaré (left) receiving
his SEMNI O. C. Zienkiewicz
award 2019

SEMNI O. C. Zienkiewicz award
The highest distinction provided biannually by SEMNI to personalities in
the field of numerical methods in engineering is the O. C. Zienkiewicz
prize. In this occasion, the award goes to:• Prof. Manuel Doblaré, from the University of Zaragoza.
Prof. Manuel Doblaré is a member of the Spanish Royal Academyof Engineering and a renowned specialist in the field of numericalmethods, biomechanics and mechanobiology. Prof. Doblaré will
receive a diploma and a plaque during the gala dinner of the next
Conference on Numerical Methods, CMN, to be held in Guimarães,Portugal, next July 1-3rd, 2019.
Traditionally, SEMNI O. C. Zienkiewicz awardees deliver a plenary
lecture during this same conference. In this case, the conference isentitled From Data-Driven modeling to Physically-Constrained Deep
Learning in Predictive Continuum Physics, where he will overview theresults of his research in the field.
41 iacm expressions 46/20

PCM-CMM 2019
The 4 th Polish Congress of Mechanics&
23 rd International Conference on Computer Methods in Mechanics

On September 8-12 2019, a joint scientific event including the 4th Polish Congress of
Mechanics (PCM) and the 23rd International Conference on Computer Methods in
Mechanics (PCM-CMM 2019) was held in Cracow in Auditorium Maximum, the congress
centre of the Jagiellonian University. The PCM-CMM 2019 Congress was co-organizedby the Polish Society of Theoretical and Applied Mechanics and Polish Association for
Computational Mechanics (PACM). The congress was organized under auspices of theEuropean Community on Computational Methods in Applied Sciences (ECCOMAS) as the
ECCOMAS Special Interest Conference.
The 4th Polish Congress of Mechanics (PCM) continues the tradition of scientific meetings
on mechanics, initiated around 12 years ago. The Congress was aimed to present thecurrent state of the art research in all disciplines of mechanics, solid and fluid mechanics,
computational mechanics, applied mathematics and physics, as well as structural
mechanics and mechanical engineering. This and previous editions of the PCM congress
were organized jointly with the CMM conferences. The CMM conference series is devoted
to numerical methods and their applications to the study of the various phenomena inmechanics. The CMM conferences are organized regularly every two years, starting in
1973.
Prof. Michał Kleiber (Polish Academy of Sciences, President of ECCOMAS) acted as
the Congress Chairman, Prof. Włodzimierz Kurnik (Warsaw University of Technology)
as the Congress Vice-President, the Honorary Chairmanship was bestowed upon
Prof. Witold Gutkowski (Polish Academy of Sciences). The Scientific Committee waschaired by Prof. Tadeusz Burczyński (Institute of Fundamental Technological Researchof Polish Academy of Sciences) and the Organizing Committee was chaired by
Prof. Błażej Skoczeń from Cracow University of Technology. Andrzej Duda, President of
the Republic of Poland has kindly granted Honorary Patronage to the Congress, andJaroslaw Gowin, Minister of Science and Higher Education, has chaired the Congress
Honorary Committee. Both President Andrzej Duda and Minister Jaroslaw Gowin attended
and delivered speeches at the opening ceremony.
The scientific programme of the PCM-CMM 2019 Congress was composed of 12 plenary
lectures, 4 thematic sessions and 22 mini-symposia. There were 550 participants from
Poland and 27 countries from Europe, Asia and North America. The following distinguishedscientists held the interesting plenary lectures on current research topics:● Leszek Demkowicz, USA, Progress Report on the DPG Method● Fionn Dunne, Great Britain, Microstructurally-Sensitive Fatigue Crack Nucleation and
Growth: Dislocation Configurational Energy and Crystal Plasticity Approaches● Thomas J.R. Hughes, USA, The Isogeometric Approach to Analysis● Tomasz Lewiński, Poland, Least Weight
Structures and Least Compliant Bodies.
Optimal Layout Problems and Recovery ofUnderlying Microstructure● Shaker Meguid, Canada, Multiscale Modelingof Multifunctional Nanocomposites:
Figure 1:
Distinguished guests at the opening ceremony:third from left Andrzej Duda, President of the
Republic of Poland, fourth from left
Jaroslaw Gowin, Minister of Science and Higher
Education, first from right Michal Kleiber,
President of ECCOMAS
iacm expressions 46/20 42

for all inclusions under PACM please contact
Jerzy Rojek
jrojek@ippt.gov.pl
Opportunities and Challenges● Andreas Menzel, Germany, Electro-Viscoelasticity of Dielectric Elastomers –Experiment, Modelling and Simulation● Bernd R. Noack, Germany, Turbulence Control - Better, Faster and Easier with
Machine Learning● Jerzy Pamin, Poland, Numerical Simulation of Material Instabilities● Henryk Petryk, Poland, On Microstructural Length Scales in Single Crystals● Franz G. Rammerstorfer, Austria, Some Oddities in Plate and Shell Buckling● Meir Shillor, USA, Recent Results on Dynamic Contact, Friction, Heat, Exchange,
and Debonding● Marek Stankiewicz, Poland, Synchrotron SOLARIS - Large Research Infrastructure
and Its Mechanical Challenges
It has become a tradition that during the CMM conferences the O.C.Zienkiewicz Medal
Awarding Ceremony takes place to honour outstanding researchers in the field ofcomputational mechanics. This year’s awarding ceremony was held during the congress
opening. The 2019 laureates of the O.C. Zienkiewicz Medal are:
● Prof. Tomasz Lewiński in the category for the whole of activity,● Prof. Jerzy Pamin in the category for the whole of activity,● Prof. Thomas J.R. Hughes in the category for foreign scientists of particular merit forthe development of computational mechanics in Poland.
The awards were presented by Prof. Michał Kleiber (Commander of the Medal Chapter)and Prof. Tadeusz Burczyński (Chancellor of the Medal Chapter), during the ceremony
conducted by Prof. Jerzy Rojek (President of PACM).
During the conference there was also held the Jan Schmelter competition for young
scientists. Jury of the competition led by Prof. Waldemar Rachowicz (Cracow Universityof Technology) awarded three prizes to the following young scientists:● 1st place: Nicola A. Nodargi – University of Rome Tor Vergata● 2nd place: Tomasz Schlieter – Silesian University of Technology● 3rd place: Witold Ogierman – Silesian University of Technology
and one recognition award to Paulina Świątkiewicz from Lodz
University of Technology.
The General Assembly of PACM was held during the conference.
At the meeting, PACM president Jerzy Rojek presented a report on
PACM activities and achievements during the last term 2017-2019.
The report was accepted unanimously. Prof. Jerzy Rojek was electedpresident of PACM for the next term 2019-2021.
The PCM-CMM 2019 congress was a successful, well-organizedscientific event. More details on PCM-CMM 2019 congress can befound at the conference web site: https://pcm-cmm2019.com/en/.
The next, 24th International Conference on Computer Methods
in Mechanics will be organized by the West Pomeranian Universityof Technology Szczecin in September 2021. Prof. Ryszard Buczkowski
will chair the organizing committee. The next 5th Polish Congress of
Mechanics will be held in Gliwice (Upper Silesia) in 2023.

Figure 2:
Andrzej Duda,
President of the
Republic of Poland,delivers a speech at
the opening ceremony

Figure 3:
Michal Kleiber,
President of
ECCOMAS, delivers
a speech at the
opening ceremony

Figure 4:
Awarding Ceremony of the O.C. Zienkiewicz Medal.
From left to right:
Thomas J.R. Hughes, Jerzy Pamin, Tomasz Lewiński – recipients ofMedal, Jerzy Rojek – President of PACM,
Michał Kleiber – Commander of the Medal Chapter,
Tadeusz Burczyński – Chancellor of the Medal Chapter
43 iacm expressions 46/20

ENIEF 2019
XXIV Congress on Numerical Methods
and their Applications
Santa Fe, Argentina
5 - 7 November 2019T

he XXIV Congress on Numerical Methods and their Applications (ENIEF 2019)
took place from November 5 through November 7, 2019, at the city of Santa
Fe, Argentina. This new edition of the annual AMCA Congresses was organizedby the Research Centre on Computational Methods (CIMEC), dependent on the
National University of the Littoral (UNL) and the Argentine Council for Research in
Science and Technology (CONICET), together with the Argentine Association ofComputational Mechanics (AMCA).

Figure 1:
Opening Ceremony,from left to right:
Prof. Alberto Cardona (chairof the congress),
Prof. Enrique Mammarella(Rector of UNL),
and Víctor Fachinotti(president of AMCA)

The Organizing Committee included Prof. Alberto Cardona (chairman),Prof. Sergio Idelsohn (honorary chairman), Prof. Pablo Kler and Prof. Santiago
Márquez Damián (co-chairmen). The Scientific Committee was chaired by
Prof. Mario Storti, with Prof. Luciano Garelli and Prof. Juan Marcelo Giménez as
co-chairmen.
The Congress hosted seven invited lectures by Prof. Rainald Löhner (George
Mason University, USA), Prof. Jaime Klapp (National Institute for Nuclear
Research, Mexico), Prof. Adrian Lew, (Department of Mechanical Engineering,
Stanford University, USA), Prof. Marcela Cruchaga (University of Santiago
de Chile, Chile), Prof. Roberto Lamberts (Laboratory for Energy Efficiency in
Buildings, Federal University of Santa Catarina, Brazil), Prof. Luis Caffarelli(University of Texas at Austin, USA), andProf. Mariano Vázquez (Barcelona Supercomputing
Center and ELEM Biotech, Spain).
The Congress was held at Estación Belgrano
Conventions Center, a renewed former train station
located at an iconic place in Santa Fe, and wasattended by 300 researchers and students from
Argentina, Brazil, Chile, Colombia, Uruguay, Costa
Rica, Ecuador, Germany and Spain. Full-length
papers were submitted to a review process prior to
publication. Accepted papers have been publishedin proceedings which are openly available at thewebsite http://www.amcaonline.org.ar/mcamca

Figure 2:
Welcome cocktail on a boat through Setúbal lagoon

A special session was devoted to undergraduate
students, with awards for the best posters, that were
granted to:
1. Pablo N. Wierna, from the Applied Mechanics
Research and Development Group (GIDMA) of
the National Technological University at Cordoba,
Argentina.
2. Tomás Leschiutta, from the National
Technological University (UTN) at Santa Fe,
Argentina.
3. Facundo Castañón, from the Applied
Multiphysics Research Group (GIMAP) of the
National Technological University at Bahia Blanca,
Argentina.

iacm expressions 46/20 44

for all inclusions under AMCA please contact
Pablo A. Kler
kler@cimec.santafe-conicet.gov.ar

During the congress dinner, Prof. Luis Caffarelli received the AMCA International
Scientific Trajectory Award which was already announced last year.
The Congress also included other activities like the VIIII Meeting of OpenFOAM
users and the special session to honor Prof. Victorio Sonzogni (former presidentof AMCA) for his retirement.

Figure 5:
Prof. Luis Caffarelli receiving the AMCA Award
from Prof. Sergio Idelsohn

MECOM 2020
XXXVII Argentine Congress
for Computational Mechanics
November 3-5, 2020
Resistencia, Chaco, Argentina
CALL FOR PAPERS
The Argentine Association for Computational Mechanics (AMCA) announces
the XXXVII Argentine Congress for Computational Mechanics (MECOM 2020),
which will be held at Resistencia, Argentina, organized by the Faculty ofEngineering of the National University of the NorthEast (UNNE) and AMCA.
http://amcaonline.org.ar/mecom2020
45 iacm expressions 46/20

conference diary planner
18 - 21 Dec 2019 APCOM 2019 - 7th Asian Pacific Congress on Computational Mechanics
Venue:

Taipei, Taiwan

Contact:

http://www.apcom2019.org/

26 - 28 Feb 2020 USACM Thematic Conference - Large Scale Structural Systems and Optimal Design
Venue:

Baton Rouge, LA, USA

Contact:

http://wfm2020.usacm.org/organizers

4 - 6 March 2020 1 BICTAM-CISM Symposium on Dispersed Multiphase Flows: from Measuring to ModelingstVenue:

Beijing, China

Contact:

http://158.110.32.35/BICTAM-CISM

8 - 11 March 2020 COMSAFE 2020 - Computational Engineering & Science for Safety & Environmental Problems
Venue:

Kobe, Japan

Contact:

https://www.compsafe2020.org/

15 - 18 March 2020 IUTAM Special Event: Fluid Mechanics in the Spirit of G.K. Batchelor
Venue:

Cambridge, UK

Contact:

http://www.damtp.cam.ac.uk/events/

1 - 3 April 2020 2020 Conference of the UK Association for Computational Mechanics
Venue:

Loughborough University, UK

Contact:

http://ukacm.org/ukacm-conferences/

5 - 8 April 2020 EMI 2020-IC ASCE Engineering Mechanics Institute International Conference
Venue:

Durham, UK

Contact:

https://sites.durham.ac.uk/emi2020-ic/

9 - 11 June 2020 14 Int. Confonference on Computational Fluid Dynamics In the Oil & Gas, Metallurgical &thProcess Industries
Venue:

Trondheim, Norway

Contact:

https://www.sintef.no/cfd2020/22 - 26 June 2020 ECCM19 - European Conference on Composite Materials
Venue:

Nantes, France

Contact:www.eccm19.org

13 - 17 July 2020 BAIL 2020 - International Conference on Boundary and Interior Layers
Venue:

Buenos Aires, Argentina

Contact:

https://amcaonline.org.ar/

19 - 24 July 2020 ECCOMAS CONGRESS 2020 jointly organized with the
WCCM XIV - 14th World Congress on Computational Mechanics (IACM)
Venue:

Paris, France

Contact:

http://www.eccomas.org/

23 - 28 Aug 2020 25th ICTAM - International Congress of Theoretical and Applied Mechanics
Venue:

Milan, Italy

Contact:

https://www.ictam2020.org/

16 - 18 Sept 2020 SAHC 2020 - International Conference on Structural Analysis of Historical Constructions
Venue:

Barcelona, Spain

Contact:

http://congress.cimne.com/SAHC2020/27 - 29 Sept 2020 USACM Thematic Conference - Novel Methods in Computational Engineering and Sciences
Venue:

Berkeley, California

Contact:

http://mfem2020.usacm.org/

18 - 21 Oct 2020 USACM Thematic Conference - Isogeometric Analysis 2020
Venue:

Banff, Canada

Contact:

http://iga2020.usacm.org/

Contact:

ruth@usacm.org

19 - 21 Oct 2020 IGA2020 - Isogeometric Analysis 2020
Venue:

Banff, Canada

1 - 5 Nov 2020 International Conference on Multibody System Dynamics & Asian Conference on Multibody
Dynamics 2020
Venue:New Delhi, India

Contact:

https://iutam.org/

2 - 6 Nov 2020 IUTAM Symposium on Computational fracture mechanics in multi-field problems
Venue:Bad Honnef, Germany

Contact:

https://iutam.org/

3 - 5 Nocv 2020 MECOM 2020 - XXXVII Argentine Congress for Computational Mechanics
Venue:

Resistencia, Argentina

Contact:

http://amcaonline.org.ar/mecom2020

17 - 21 Oct 2021 21 International Symposium on Finite Element Methods in Flow Problems
stVenue:
iacm expressions 26/10 46

Hangzhou, China

Contact:

https://iacm.info/scientific-events/fef/