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Physics Informed Machine Learning Workshop

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Michael Mahoney - Why Deep Learning Works

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Benjamin Peherstorfer - Data generation for learning reduced models with operator inference

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Kevin Carlberg - Breaking Komolgorov-Width Barriers using Deep Learning

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Karthik Duraisamy - Physics constrained probabilistic learning of Koopman decompositions

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Aleksandr Aravkin - Algorithms for Nonsmooth, Nonconvex Problems in Data-Driven Discovery

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Alex Gorodetsky - Scalable Learning of Dynamical Systems

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Emily Fox - Flexibility, Interpretability, and Scalability in Time Series Modeling

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Michael Brenner - Machine Learning for Partial Differential Equations

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Charbel Farhat - Probabilistic Physics-Based Machine Learning for Digital Twins

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Kathleen Champion - Data-driven discovery of coordinates and governing equations

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Steve Brunton - Discovering interpretable and generalizable dynamical systems from data

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Benjamin Erichson - Shallow Learning for Flow Reconstruction with Limited Sensors and Limited Data

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Paris Perdikaris - Data-driven modeling of stochastic systems using physics-aware deep learning

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