Physics Informed Machine Learning
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Physics Informed Machine Learning Workshop
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Michael Mahoney - Why Deep Learning Works
Benjamin Peherstorfer - Data generation for learning reduced models with operator inference
Kevin Carlberg - Breaking Komolgorov-Width Barriers using Deep Learning
Karthik Duraisamy - Physics constrained probabilistic learning of Koopman decompositions
Aleksandr Aravkin - Algorithms for Nonsmooth, Nonconvex Problems in Data-Driven Discovery
Alex Gorodetsky - Scalable Learning of Dynamical Systems
Emily Fox - Flexibility, Interpretability, and Scalability in Time Series Modeling
Michael Brenner - Machine Learning for Partial Differential Equations
Charbel Farhat - Probabilistic Physics-Based Machine Learning for Digital Twins
Kathleen Champion - Data-driven discovery of coordinates and governing equations
Steve Brunton - Discovering interpretable and generalizable dynamical systems from data
Benjamin Erichson - Shallow Learning for Flow Reconstruction with Limited Sensors and Limited Data
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