Machine Learning for Computational Fluid Dynamics
Explore the potential of machine learning to revolutionize computational fluid dynamics in this 39-minute conference talk by Steve Brunton. Delve into key areas where machine learning can make significant impacts, including accelerating direct numerical simulations, improving turbulence closure modeling, and developing enhanced reduced-order models. Learn how incorporating physics into machine learning processes can lead to improved fluid simulations and new physical insights. Discover the importance of establishing benchmark systems, open-source software, data sharing, and reproducible research practices in harnessing machine learning's full potential for computational fluid dynamics. Gain insights into various topics such as data-driven discretizations for partial differential equations, enhancement of shock capturing schemes, convolutional LSTM for PDEs, Reynolds Averaged Navier Stokes (RANS) closure models, and sparse turbulence models.