How can machine learning help large-scale scientific simulation? Accurate simulation of fluids is important for problems like engineering design and climate modeling, but is very computationally demanding. In this talk, I'll give an overview of a line of research at Google, where we've been using end-to-end deep learning to improve approximations inside traditional numerical solvers. For 2D turbulent flows, our models are up to two orders of magnitude faster than traditional solvers with the same accuracy on the same hardware, and can still generalize to very different types of flows from those on which they were trained.
Stephan Hoyer is a staff engineer at Google Research. He works on deep learning for science, with a focus on physical simulations and applications in climate/weather modeling. His research centers on the hypothesis that automatic differentiation software, hardware accelerators and deep learning are poised to transform traditional scientific computing, by vastly accelerating and improving existing numerical models. He also frequently contributes to open source tools for scientific computing in Python, including JAX and NumPy. Before Google, he was a data scientist at The Climate Corporation, and received his Ph.D in physics from UC Berkeley.
The AI Seminar is generously sponsored by Morgan Stanley.
Zoom Participation. See announcement.