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
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