CFDML2021: Second International Workshop on the Application of Machine Learning Techniques to Computational Fluid Dynamics and Solid Mechanics Simulations and Analysis

Held in conjunction with the International Supercomputing Conference (ISC) High Performance 2021, July 2, 2021

Note: This year the workshop will be held virtually

Workshop Scope

The combination of computational fluid dynamics (CFD) with machine learning (ML) is a recently emerging research direction with the potential to enable the solution of so far unsolved problems in many application domains. Machine learning is already applied to a number of problems in CFD, such as the identification and extraction of hidden features in large-scale flow computations, finding undetected correlations between dynamical features of the flow, and generating synthetic CFD datasets through high-fidelity simulations. These approaches are forming a paradigm shift to change the focus of CFD from time-consuming feature detection to in-depth examinations of such features, and enabling deeper insight into the physics involved in complex natural processes.

The workshop is designed to stimulate this research by providing a venue to exchange new ideas and discuss challenges and opportunities as well as expose this newly emerging field to a broader research community. It brings together researchers and industrial practitioners working on any aspects of applying ML to the CFD and related domains, in order to provide a venue for discussion, knowledge transfer, and collaboration among the research community.

Workshop Format

Live Keynote will follow by pre-recorded video presentations. All speakers will be available for a virtual Q&A at their designated times. Link to connect to the event will be posted here shortly.

Program

The list of activities occurring on July 2, 2021

Live Keynote: Discovering hidden fluid mechanics using PINNs and DeepONetsGeorge Karniadakis, Brown University
14:00-14:30 (Central Europe Time) / 08:00-08:30 (U.S. Eastern Time)
Session chair: Eloisa Bentivegna, IBM Research Europe, UK

Keynote Q&A
14:30-14:40 (Central Europe Time) / 08:30-08:40 (U.S. Eastern Time)

Session 1: Fluid mechanics with turbulence, reduced models, and machine learning (Q&A with the authors)
14:40-16:00 (Central Europe Time) / 08:40-10:00 (U.S. Eastern Time)
Session chair: Ashley Scillitoe, The Alan Turing Institute, UK

Nonlinear mode decomposition and reduced-order modeling for three-dimensional cylinder flow by distributed learning on FugakuKazuto Ando, Keiji Onishi, Rahul Bale, Makoto Tsubokura, Akiyoshi Kuroda and Kazuo Minami
Watch the presentation | View the presentation slides (PDF)

Reconstruction of mixture fraction statistics of turbulent jet flows with deep learningMichael Gauding and Mathis Bode
Watch the presentation | View the presentation slides (PDF)

Reservoir computing in reduced order modeling for chaotic dynamical systems — Alberto Costa Nogueira Junior, Felipe de Castro Teixeira Carvalho, João Lucas de Sousa Almeida, Andres Codas, Eloisa Bentivegna and Campbell D Watson
Watch the presentation | View the presentation slides (PDF)

A data-driven wall-shear stress model for LES using gradient boosting decision treesSarath Radhakrishnan, Lawrence Adu-Gyamfi, Arnau Miró, Bernat Font and Joan Calafell
Watch the presentation | View the presentation slides (PDF)

Session 2: Novel methods development in machine learning and fluid simulation (Q&A with the authors)
16:00-17:00 (Central Europe Time) / 10:00-11:00 (U.S. Eastern Time)
Session chair: Alberto Costa Nogueira Junior, IBM Research Brazil, Brazil

Lettuce: PyTorch-based lattice Boltzmann frameworkMario C. Bedrunka, Dominik Wilde, Martin Kliemank, Dirk Reith, Holger Foysi and Andreas Krämer
Watch the presentation | View the presentation slides (PDF)

Novel DNNs for stiff ODEs with applications to chemically reacting flowsThomas Brown, Harbir Antil, Rainald Lohner, Fumiya Togashi and Deepanshu Verma
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Machine-learning-based control of perturbed and heated channel flowsMario Rüttgers, Moritz Waldmann, Wolfgang Schröder and Andreas Lintermann
Watch the presentation | View the presentation slides (PDF)

Session 3: Confluence of machine learning and fluid simulation applications (Q&A with the authors)
17:00-18:00 (Central Europe Time) / 11:00-12:00 (U.S. Eastern Time)
Session chair: Charalambos Chrysostomou, The Cyprus Institute

Physics informed machine learning for fluid-structure interaction — Qiming Zhu, Jinhui Yan
Watch the presentation | View the presentation slides (PDF)

Film cooling prediction and optimization based on deconvolution neural networkYaning Wang, Shirui Luo, Wen Wang, Guocheng Tao, Xinshuai Zhang and Jiahuan Cui
Watch the presentation | View the presentation slides (PDF)

Turbomachinery blade surrogate modeling using deep learningShirui Luo, Jiahuan Cui, Vignesh Sella, Jian Liu, Seid Koric and Volodymyr Kindratenko
Watch the presentation | View the presentation slides (PDF)

Workshop Co-Chairs and Program Committee

  • Volodymyr Kindratenko, National Center for Supercomputing Applications, USA
  • Andreas Lintermann, Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany
  • Charalambos Chrysostomou, The Cyprus Institute, Cyprus
  • Jiahuan Cui, Zhejiang University, China
  • Eloisa Bentivegna, IBM Research Europe, UK
  • Ashley Scillitoe, The Alan Turing Institute, UK
  • Morris Riedel, University of Iceland, Iceland
  • Jenia Jitsev, Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany
  • Seid Koric, National Center for Supercomputing Applications, USA
  • Shirui Luo, National Center for Supercomputing Applications, USA
  • Alberto Costa Nogueira Junior, IBM Research Brazil, Brazil
  • Jeyan Thiyagalingam, Science and Technology Facilities Council, UK
  • Nikos Savva, The Cyprus Institute, Cyprus