Physics Informed Machine Learning Workshop

*University of Washington,*

*Seattle*

June 6-7, 2019

June 6-7, 2019

Local Organizers

Steven L. Brunton (Mechanical Engineering)

J. Nathan Kutz (Applied Mathematics)

Plenary Speakers

Steven L. Brunton (Mechanical Engineering)

J. Nathan Kutz (Applied Mathematics)

Plenary Speakers

Michael Mahoney, UC Berkeley

Why Deep Learning Works: Heavy-Tailed Random Matrix Theory as an Example of Physics Informed Machine Learning

Michael Brenner, Harvard

Machine Learning for Partial Differential Equations

Emily Fox, Washington

Flexibility, Interpretability, and Scalability in Time Series Modeling

Charbel Farhat, Stanford

Probabilistic Physics-Based Machine Learning for Digital Twins

Minisymposia Sessions

Control and Optimization

Sasha Aravkin, Washington

Algorithms for Nonsmooth, Nonconvex Problems in Data-Driven Discovery

Benjamin Erichson, Berkeley

Shallow Learning for Flow Reconstruction with Limited Sensors and Limited Data

Alex Gorodetsky, Michigan

Scalable Learning of Dynamical Systems

Data-Driven Model Discovery

Steven Brunton, Washington

Discovering interpretable and generalizable dynamical systems from data

Kathleen Champion, Washington

Data-driven discovery of coordinates and governing equations

Paris Perdikaris, Pennsylvania

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

Reduced Order Modeling

Kevin Carlberg, Sandia

Breaking Komolgorov-Width Barriers using Deep Learning: Projection Dynamical Systems on Nonlinear Manifolds Constructed by Convolutional Autoencoders

Karthik Duraisamy, Michigan

Physics constrained probabilistic learning of Koopman decompositions

Benjamin Peherstorfer, Courant NYU

Data generation and time-delay corrections for learning reduced models with operator inference