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Physics simulation has become the third pillar of science and engineering, along with theory and experiments. The overarching objective of my cross-disciplinary research is to democratize physics simulation. This is achieved through a thoughtful fusion of cutting-edge AI methodologies and classical numerical methods. In this talk, I will introduce two research threads that align with this overarching theme. These threads will harness various machine learning tools (e.g., neural fields) to advance physics simulations’ accuracy and speed. A recurring theme in all two threads is the exceptional generalization capabilities of these ML-enhanced simulations, thanks to the careful incorporation of partial differential equations (PDEs) as an inductive bias.

Reception in the lobby of MSB at 3:30pm, preceding the talk