Machine Learning, Dynamical Systems and Control

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Physics Informed Machine Learning Workshop

University of Washington, Seattle
June 6-7, 2019


Local Organizers
Steven L. Brunton (Mechanical Engineering)
J. Nathan Kutz (Applied Mathematics)


Plenary Speakers

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Michael Mahoney, UC Berkeley
Why Deep Learning Works: Heavy-Tailed Random Matrix Theory as an Example of Physics Informed Machine Learning


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Michael Brenner, Harvard
Machine Learning for Partial Differential Equations
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Emily Fox, Washington
Flexibility, Interpretability, and Scalability in Time Series Modeling
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Charbel Farhat, Stanford
Probabilistic Physics-Based Machine Learning for Digital Twins

Minisymposia Sessions

Control and Optimization

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Sasha Aravkin, Washington
Algorithms for Nonsmooth, Nonconvex Problems in Data-Driven Discovery
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Benjamin Erichson, Berkeley
Shallow Learning for Flow Reconstruction with Limited Sensors and Limited Data
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Alex Gorodetsky, Michigan
Scalable Learning of Dynamical Systems

Data-Driven Model Discovery


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Steven Brunton, Washington
Discovering interpretable and generalizable dynamical systems from data
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Kathleen Champion, Washington
Data-driven discovery of coordinates and governing equations
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Paris Perdikaris, Pennsylvania
Data-driven modeling of stochastic systems using physics-aware deep learning


Reduced Order Modeling


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Kevin Carlberg, Sandia
Breaking Komolgorov-Width Barriers using Deep Learning: Projection Dynamical Systems on Nonlinear Manifolds Constructed by Convolutional Autoencoders
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Karthik Duraisamy, Michigan
Physics constrained probabilistic learning of Koopman decompositions
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Benjamin Peherstorfer, Courant NYU
Data generation and time-delay corrections for learning reduced models with operator inference