Kobe Opportunities for EU-Japan Research Collaboration
Kobe University, RIKEN, and European Institutes

Tuesday 5th July 2022
L42 Business Center

SPEAKERS’ BIOGRAPHIESAND ABSTRACTS

Session 1: Kobe as a leading base for computational sciences
Speaker

09:00-10:30

Makoto Tsubokura, Kobe University and RIKEN (Japan)
Mario Rüttgers, Jülich Supercomputing Centre (Germany)
Alfonso Valencia, Barcelona Supercomputing Center (BSC) (Spain)

Coffee break

10:30-11:00

Session 2: Place-based innovation through international cooperation

11:00-12:30

Speaker

Satoru Oishi, Kobe University and RIKEN (Japan)
Elpida Kolokytha, Aristotle University, Thessaloniki (Greece)
Audrey Gailler, Commissariat à l’énergie atomique (CEA)-DAM/DIF (France)

Networking lunch

12:30-13:30

Digital Transformation of Droplet/Aerosol Infection Risk Assessment andEvaluation of Countermeasures Realized on the Supercomputer “Fugaku”
Makoto Tsubokura
RIKEN Center for Computational Science
Graduate School of System Informatics, Kobe University
Mtsubo[@]riken.jp

Virus droplet infection caused by sneezing, coughing, or talking is strongly influenced by theflow, temperature and humidity of the air around an infected person and potential victims. Especiallyin the case of COVID-19, possibility of aerosol infection by atomized droplets is suggested, inaddition to the usual direct droplet infection. Because smaller aerosol particles drift in the air for alonger time, it is imperative for the precise infection risk assessment to predict their scattering routeand to estimate how surrounding airflow affects the infection. This is also the case when proposing
countermeasures to reduce the infection risk. The fastest supercomputer in 2020, “Fugaku”, has not
only achieved digital transformation of epidemiology in allowing end-to-end, detailed quantitativemodeling of COVID-19 transmissions for the first time, but also transformed the behavior of theentire Japanese public through its detailed analysis of transmission risks in multitudes of societal
situations entailing heavy risks. A novel aerosol simulation methodology was synthesized out of a
combination of a new computational fluid dynamics methods meeting industrial demands, “CUBE”,
which not only allowed the simulations to scale massively with high resolution required for
micrometer virus-containing aerosol particles, but also extremely rapid time-to-solution due to its
ability to generate the digital twins representing multitudes of societal situations in minutes not week,
attaining true overall application high performance; such simulations have been running for the past
2 years on “Fugaku”, cumulatively consuming top supercomputer-class resources and the resultcommunicated by the media as well as becoming official public policies. In this talk, the details of
the digital transformation of virus droplet/aerosol infection risk assessment and countermeasureproposal realized by the collaboration of “Fugaku” and “CUBE” will be introduced.

Makoto Tsubokura has been the professor of computational fluid dynamics at theGraduate School of System Informatics, Kobe University since 2015, and the leader ofComplex Unified Simulation Research Team at the Center of Computational Science,RIKEN since 2012. After earning a PhD from the University of Tokyo in 1997, he firstbecame a lecturer at the Tokyo Institute of Technology in 1999, then associate professor
at the University of Electro-Communications in 2003, then at Hokkaido University in2007, and then obtaining current positions. He specializes in research and developmentof mathematical modeling and simulation algorithms for thermal fluids in turbulent state,and their expansion to massively parallel environment on high-end supercomputers. Heis a fellow of the Japan Society of Mechanical Engineers, the Japan Society of FluidMechanics, and Society of Automotive Engineers of Japan. He has been a member of theEngineering Academy of Japan since 2020

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Combining Flow Simulations and Machine Learning Techniques
for Improved Diagnostics and Treatments in Rhinology
Mario Rüttgers
Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University,Jülich Supercomputing Centre, Forschungszentrum Jülich, m.ruettgers[@]fz-juelich.de

The integration of numerical flow simulations into daily clinical practice represents a key aspect for the improvementof diagnostics and treatments in rhinology. Such an integration is, however, only feasible if the involved numericalmethods provide accurate results, are fast, and fully automated. This presentation describes how highly-resolved andparallelized lattice-Boltzmann simulations are combined with machine-learning-based techniques to automatize
workflows for analyzing respiratory flows. Two types of applications are presented. The first application
demonstrates how the complete process from medical imaging to the visualization of simulation results can beautomatized[1]. An automated pipeline is presented that has already been used for multiple studies that require many
nasal respiration simulations[2,3]. This pipeline circumvents the otherwise highly time-consuming semi-manual
extraction of the geometry, which is obviously impractical for clinical use. The second application is a virtual surgery
that helps surgeons in their decision-making process. Three different approaches for virtual surgeries are presented
and evaluated, i.e., (i) an approach based on 3D-prints, (ii) one that computes fluid mechanical properties for allstates between pre- and post-surgical geometries[4], and (iii) a reinforcement-learning-based approach that optimizes
a shape with respect to fluid mechanical parameters[5].
References:[1] M. Rüttgers, M. Waldmann, W. Schröder, and A. Lintermann, “A machine-learning-based method for automatizing lattice-boltzmann simulations of respiratory
flows,” Applied Intelligence, 2022.
[2] H. Aljawad, M. Rüttgers, A. Lintermann, W. Schröder, and K. C. Lee, “Effects of the nasal cavity complexity on the pharyngeal airway fluid mechanics: Acomputational study,” Journal of Digital Imaging, vol. 34, pp. 1120 – 1133, 2021.
[3] H. Aljawad, M. Rüttgers, W. Schröder, A. Lintermann, and K. C. Lee, “Long-term evaluation of the upper airway following mandibular setback surgery inclass iii patients using computational fluid dynamics,” submitted to American Journal of Orthodontics & Dentofacial Orthopedics, 2022.
[4] M. Waldmann, M. Rüttgers, A. Lintermann, and W. Schröder, “Virtual surgeries of nasal cavities using a coupled lattice-boltzmann-levelset approach,”
Journal of Engineering and Science in Medical Diagnostics and Therapy, vol. 5, no. 3, 2022.
[5] M. Rüttgers, M. Waldmann, W. Schröder, and A. Lintermann, “Machine-learning-based control of perturbed and heated channel flows,” in High Performance
Computing: ISC High Performance Digital 2021 International Workshops, Frankfurt Am Main, Germany, June 24 -July 2, 2021.

Mario Rüttgers received the B.S. degree in mechanical engineering from RWTH Aachen
University in 2015. He attended full-time Korean language courses at Chonnam NationalUniversity (CNU) in Gwangju, South Korea, from 2015 to 2017 and received the M.S. degree
in mechanical engineering from Pohang University of Science and Technology (POSTECH) in2019. Since 2019, he has been working as a researcher at the Institute of Aerodynamics andChair of Fluid Mechanics at RWTH Aachen University, and at the Jülich Supercomputing
Centre (JSC) at Forschungszentrum Jülich. At the same time, he is a PhD student at RWTH
Aachen University and associated with the Helmholtz School for Data Science in Life, Earthand Energy (HDS-LEE). His research focuses on combining CFD and machine learning.

2High performance computing in biomedicine. The BSC perspective.
Alfonso Valencia

ICREA Research Professor
Life Sciences Dept. Director, Barcelona Supercomputing Center (BSC)
alfonso.valencia[@]bsc.es
The new generation of High Performance Computing systems – the exascale generation- open
interesting challenges and possibilities for the applications in biomedicine. At the technical level,
the general challenges include the heterogenous nature of the new systems and processors, as wellas the complexity of the operations with large quantities of personal and confidential data.
Beyond the data access and handling issues, the new infrastructures are essential for three of theapplication areas in which BSC is interested:-Natural Language Processing for the extraction of information from medical documents.

In our, experience the development of BERT-like approaches in specific areas, e.g., a COVID19 requires substantial investment in terms of corpus collection, expert labelling, computing
time for training and depuration.
-Artificial Intelligence and Machine Learning applications, for the prediction of theconsequences of mutations, disease classification, and the prediction of disease trajectories
based on the combination of medical and molecular data.
-The simulation of biomedical systems from the organs to the cell level- the HumanDigital Twin. The European HPC/Exascale Centre of Excellence for Personalised Medicine(PerMedCoE https://www.hpccoe.eu/permedcoe/) coordinated by BSC, aims to set the basis forthe simulations of complex systems, e.g., tumours or organoids, embedding intracellular level(metabolic, signalling and regulatory networks) into inter-cellular environments, i.e., agent
based simulations accounting for cell-cell and cell-environment interactions.
These and other areas of Biomedicine posse significant challenges that will be better addressed bycollaboration, like for example the possible collaboration with R-CCS at RIKEN at Kobecity/region.
Prof. Alfonso Valencia is ICREA Research Professor and Director of the Life Sciences
Department at the Barcelona Supercomputing Centre (BSC), Director of the Spanish
National Bioinformatics Institute (INB) and head of the Spanish Node of the European
Bioinformatics Infrastructure ELIXIR. He is a member of the European MolecularBiology Organisation (EMBO), and former President of the International Society for
Computational Biology (ISCB). He has served in numerous Scientific Advisory Boards,including the Innovative Medicines Initiative (IMI), of which he is currently Vice
Chair, EMBL, the Swiss Institute for Bioinformatics, among others. He is Co-Executive
Editor of journal Bioinformatics and he has published more than 450 articles with an hindex of 97 (Scopus profile).

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Breaking walls to make a safe and secured society based on simulation
Satoru Oishi
RCUSS, Kobe University; RIKEN Center for Computational Science
Tetsu[@]phoenix.kobe-u.ac.jp

Many natural disasters and anthropogenic hazards surround us. For example, Japan has
earthquakes, tsunamis, volcanic eruptions, typhoons, long-lasting rainfalls, and mudslides. Someparts of the world may have frequent and more devastating fires when it becomes dryer, or someparts may have an incredible amount of rain in the future. The recent and upcoming climate willchange the types of natural hazards. Our society should adapt to the change to decrease damage.
In order to adapt to the change or in order to prepare for less frequent hazards, we use physicsbased simulation. Physics-based simulation gives detailed information about damage whensomething happens, even though it is difficult to predict when it will happen. Moreover, physicsbased simulation counts the relationship among stakeholders having the same driving force, forexample, water resource shortage and frequent fire, sea-level increase and increased risk of hightide.
However, making comprehensive physics-based simulation is not easy; even one-way
information paths like weather information from meteorological simulation to high tide simulation
because information formats between them are not uniform. Technology progression and legacy
constructions exist at the same time. It prevents easier data exchange. We developed a “Data
Processing Platform (DPP)” to transform data with less effort for developing software. The DPP
will automatically connect user-defined libraries and third-party libraries to transform data fromone source of information to a format required by the other software.
We archived to make a three-dimensional (3D) model of concrete bridges from their twodimensional (2D) CAD data by using DPP. Moreover, DPP has created input data for FEM of thebridges from the same 2D CAD by adding a library. The library has a function to transform theformat and create files for input files of FEM simulation. It decreased the time to make anotherapplication that reads 2D CAD, and extracts required information. The talk will explain the basic
concept, case studies, and benefits.
Satoru Oishi obtained a Dr. of Engineering from Kyoto University in 1998. He was a
research associate of Disaster Prevention Research Institute, Kyoto University (19932000), conducting the fundamental mechanism of cumulus cloud generation anddevelopment by using numerical simulation of the cloud-resolving model. He was anassociate professor at the University of Yamanashi (2000-2009), conducting riverengineering and Sabo engineering. He also conducted many field studies abroad. He has
been a professor at Kobe University (2009-present). He mainly focuses on the applicationof remote sensing techniques to disaster prevention. As a team leader of RIKEN R-CCS(2017-present), he is working to promote high-performance computing simulation ofdisasters, including earthquakes, tsunamis, and weather-related disasters.

4Integrated Approach to Urban Water Management and Safe Cities
Elpida Kolokytha
Department of Civil Engineering, Division of Hydraulics, Aristotle University of Thessaloniki
In our complex, interdependent, and rapidly changing world which faces multiple crises, newstrategies and new paradigms are needed to deal with both the global environmental changes due toresources limited supply factors (water, land, air) and also the anthropogenic changes ofoverpopulation, mega-urbanization and severe climate change. More than half of the world’spopulation live in cities. Water is central in both sustainable and safe cities. In this sense, a newunderstanding of “security” in an interrelated context of social-economic-political-environmentalconditions, closely associated to the emerging new paradigm of green development is gaining
increasing attention. Integrated urban water management with emphasis to resiliency, efficiency andwater quality underline our approach. Water-related disaster risks, water scarcity, water availability,
flood risk are of major importance for resiliency. Ageing infrastructure, leakages, metering, watercharges, water reuse form critical components of efficiency whereas water quality is related to health,sanitation, drinking water and pollution, all in all are part of the integrated water management andplanning. Cities need to start investing in adaptation measures based on long-term visions andstrategies, and in sharing best practices.
Safe cities require a strategy that combines "envisioning", "empowerment", "enactment". To
achieve these, international cooperation, technology transfer and synergies are important. Moreover,
it requires “a journey of transformation” based on the re-definition of the city’s infrastructure andservices. Water acts as the connector incorporating the nexus between energy, food, waste, transportand other parts of the built environment. New adaptive governance is required to new conditions,residents should be actively engaged to decision making, technology and “big data” work as a“smart” tool in order the cities to become safe, sustainable and resilient living environments.

Elpida Kolokytha obtained a Ph.D. at Aristotle University of Thessaloniki (AUTh) in1999 and she is Professor in Water Resources Management and Engineering in AUTh.
Good communication skills gained as teacher, Director of water Center, scientific project
responsible, expert, EU evaluator grants. She has served as Head of Div. of Hydraulics& Environmental Engineering, Dept. of Civil Engineering, AUTh (2019-2021), Head ofAUTh Water Center (2011-2016), Director of AUTH UNESCO C2C Center for
Integrated Water Resources Management (2016 -today), Chair of IAHR WRM TC Group(2019-today), General Secretary of the Administrative Board of the Science Center andTechnology Museum “NOESIS” (2010-2014) https://www.noesis.edu.gr/. Coordinatorof (6) IMOU in AUTh with Universities in Cyprus, Italy, Japan, Israel, Kazakhstan, USA.

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The challenge of real-time forecasting of coastal tsunami impact within theFrench tsunami warning center (CENALT)
Audrey Gailler
CEA, DAM, DIF, F-91297 Arpajon, France
Audrey.gailler[@]cea.fr

For tsunami warning centers, obtaining real-time coastal inundation prediction maps in near-fieldor regional context appears as a major challenge, increasingly discussed within the European andinternational community. Currently, tsunami warning systems are mainly focused on the firstparameters of the earthquake and the tsunami models provided only give information at the scale ofthe corresponding hazard basin. No values of water heights at the coast are calculated, because unlikein the deep ocean, the full simulation of tsunami propagation at the coast requires a nonlinear modelwith fine resolution bathymetry/topography to cope with the complex coastal morphology.
Therefore, it imposes, despite a parallelization of the codes and the power of the computers used, aconsiderable computation time (> 45 min) which may not be suitable for real-time tsunami
forecasting in the near field or regional. In this context, the rapid determination of water heights atthe coast, and even the estimation of run-ups, are among the objectives of the CENALT to improve
the level of expertise and information that can be provided his correspondents.
To overcome the problem of the race against time for the determination of the water level at thecoast, warning centers are developing tools for rapid prediction of the amplitude of coastal tsunamis,
calculated from empirical laws. This type of linear approximation provides tsunami heights along
the coastline very quickly, with error bars on the calculated values of a factor of 2 at best. Suchapproaches are suitable for the near-field context, but flooding estimation is lacking.
Applications of deep learning methods for geosciences are newly being adapted in an innovative
way for near-field tsunami forecasting, and would allow adding complexity to the predicted models(run-up, local effects, currents) compared to amplification law approaches that do not capture thesethree aspects. The objective is to develop a tool to quickly transform a deep ocean tsunami simulation
result into a coastal inundation model and associated velocity field. Several learning methods (MLP,CNN) are being explored using dedicated libraries and toolboxes, with first encouraging results.
Audrey Gailler obtained a PhD in marine geophysics at the University of Nice in 2005.
She has specialized in wide-angle seismic data acquisition, processing and numericalmodeling, and held postdoc positions at IPGP (2006), University of Birmingham (2007)
and Ifremer (2008-09). Since 2010, she is geophysicist at CEA and tsunami expert since2021. Her expertise at CEA belongs to tsunami simulation with respect to Early Warningand Hazard Assessment, and to the modeling of historical tsunami triggered byearthquakes and/or landslides. She takes an active part in the implementation ofsimulation systems for tsunami forecasting within the French tsunami warning center(CENALT). She collaborates in several European and international projects dedicated totsunami research, and co-chairs the Working Group I of the ICG/NEAMTWS since 2018.

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