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Neural PDE: Towards AI-Enhanced Physics Simulation

LUNCH: Lunch will be served at 11:45 AM. ABSTRACT: Physics simulation has become the third pillar of science and engineering, along with theory and experiments. The overarching objective of my […]

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Feb 5

February 5, 2024

12:00 pm - 12:00 pm

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  • LSRC D106

LUNCH:
Lunch will be served at 11:45 AM.
ABSTRACT:
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 three research threads that align with this overarching theme. These threads will harness various machine learning tools (e.g., neural fields) to improve physics simulations’ (1) accuracy, (2) speed, and (3) accessibility. A recurring theme in all three 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.
SPEAKER BIO:
Peter Yichen Chen is a postdoctoral researcher at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), working with Professor Wojciech Matusik. He earned his Ph.D. in computer science from Columbia University under the guidance of Professor Eitan Grinspun. Before this, Peter was a Sherwood-Prize-winning mathematics undergraduate at UCLA. His research empowers 3D content creation for artists, enhances design/fabrication/control for engineers, and aids material discovery for scientists. He publishes in machine learning, computer graphics, scientific computing, mechanics, and robotics.