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

Organizers:

• Eloisa Bentivegna (IBM Research Europe)
• Charalambos Chrysostomou (The Cyprus Institute)
• Jiahuan Cui (Zhejiang University)
• Volodymyr Kindratenko (National Center for Supercomputing Applications)
• Andreas Lintermann (FZ Jülich)
• Ashley Scillitoe (The Alan Turing Institute)

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Abstract:

Combination of computational fluid dynamics (CFD) with machine learning (ML) is a newly emerging research direction with the potential to enable solving so far unsolved problems in many application domains. This workshop aims to demonstrate the use of high-fidelity CFD simulations to generate data and utilize it to train ML models to better predict the underlying physics in fluid dynamics utilizing the breakthrough in computational power, the evolution of data science techniques, and the ability to generate terabytes of data from high-fidelity simulations. ML techniques have the potential to support the identification and extraction of hidden features in large-scale flow computations, hence allowing to shift the focus from time-consuming feature detection to in-depth examinations of such features. Furthermore, ML techniques have the ability to find undetected correlations between phenomena in the flow, which will lead to deeper insight of the physics involved in complex natural processes. Apart from pure fluid dynamic, other research areas, such as constitutive modeling of heterogeneous materials, multiphase flow modelling, dynamics of the atmospheric, ocean, and climate system, and combustion/chemical reactions are working on similar techniques. The workshop will stimulate this research by providing a venue to exchange new ideas and discuss challenges and opportunities.

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