The 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. Machine learning is already applied to a number of problems in CFD, such as 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.

Apart from pure fluid dynamics, the community is embracing ML in a number of areas, such as constitutive modeling of heterogeneous materials, multiphase flow modeling, dynamics of the atmospheric, ocean, and climate system, and combustion/chemical reactions.

The workshop will 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. We aim to bring 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.