Geometric and Physics Stucture Preservation in Scientific Machine Learning

Nat Trask, Associate Professor in Mechanical Engineering and Applied Mechanics at Penn Engineering, will share about his work constructing real-time digital twins built upon a data-driven finite element exterior calculus; constructing auto-regressive integrators with guaranteed long-term stability independent of rollout length; and constructing data-driven particle models built upon metriplectic bracket theory, which preserve emergent statistical mechanics.

Lunch will be served.

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