First International Workshop on the Application of Machine Learning Techniques to Computational Fluid Dynamics Simulations and Analysis (CFDML)

Held in conjunction with the International Supercomputing Conference (ISC) High Performance 2020 Digital, June 25, 2020, Frankfurt, Germany

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

Pre-recorded video presentations and slides are available below. All speakers will be available for a panel and virtual Q&A at 3:00 p.m. CEST (8:00 a.m. CDT, 9:00 p.m. CST). Please use this link to connect to the virtual panel and Q&A.

Program

Unsupervised Learning of Particle Image Velocimetry
Mingrui Zhang and Matthew Piggott
Watch the presentation | View the presentation slides (PDF)

Complete Deep Computer-Vision Methodology for Investigating Hydrodynamic Instabilities
Re'Em Harel, Matan Rusanovsky, Yehonatan Fridman, Assaf Shimony and Gal Oren
Watch the presentation | View the presentation slides (PDF)

Data-Driven Techniques to Enhance and Supplement Computational Fluid Dynamics Prediction Capabilities
Philipp Bekemeyer, Florian Jäckel and Cornelia Grabe
Watch the presentation | View the presentation slides (PDF)

Prediction of Acoustic Fields using a Lattice-Boltzmann Method and Deep Learning
Mario Rüttgers, Seong-Ryong Koh, Jenia Jitsev, Wolfgang Schröder and Andreas Lintermann
Watch the presentation | View the presentation slides (PDF)

Reduced Order Modeling of Dynamical Systems using Artificial Neural Networks Applied to Water Circulation
Alberto Costa Nogueira Junior, João Lucas de Sousa Almeida, Guillaume Auger and Campbell D Watson
Watch the presentation | View the presentation slides (PDF)

Parameter Identification of RANS Turbulence Model using Physics-Embedded Neural Network
Shirui Luo, Madhu Vellakal, Seid Koric, Volodymyr Kindratenko and Jiahuan Cu
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, 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
  • Madhu Vellakal, National Center for Supercomputing Applications, USA
  • Jeyan Thiyagalingam, Science and Technology Facilities Council, UK