Held in conjunction with the International Supercomputing Conference (ISC) High Performance 2020 Digital, June 25, 2020, Frankfurt, Germany
ISC 2020 : International Supercomputing Conference (ISC) CFDML20 proceedings are now available online.
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
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Data-Driven Techniques to Enhance and Supplement Computational Fluid Dynamics Prediction Capabilities
Philipp Bekemeyer, Florian Jäckel and Cornelia Grabe
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Prediction of Acoustic Fields using a Lattice-Boltzmann Method and Deep Learning
Mario Rüttgers, Seong-Ryong Koh, Jenia Jitsev, Wolfgang Schröder and Andreas Lintermann
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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
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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