Physics Informed Machine Learning

Physics Informed Machine Learning

Brunton Lab, University of Washington

Discovering physical laws and governing dynamical systems is often enabled by first learning a new coordinate system where the dynamics become simple. This is true for the heliocentric Copernican system, which enabled Kepler's laws and Newton's F=ma, for the Fourier transform, which diagonalizes the heat equation, and many others.

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Prof. Steven L. Brunton
Prof. Steve Brunton
Prof. Steve Brunton
Pravinkumar Ghodake
Omid Z. Mehdizadeh PhD
Dr. Ana Larrañaga Janeiro
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Brunton Lab
Brunton Lab
University of Washington

AI/ML+Physics: Recap and Summary

Steve Brunton, University of Washington
University of Washington

AI/ML+Physics Part 5: Employing an Optimization Algorithm

Steve Brunton, University of Washington
University of Washington

AI/ML+Physics Part 4: Crafting a Loss Function

Steve Brunton, University of Washington
University of Washington

AI/ML+Physics Part 3: Designing an Architecture

Steve Brunton, University of Washington
University of Washington

AI/ML+Physics Part 2: Curating Training Data

Steve Brunton, University of Washington
University of Washington

AI/ML+Physics Part 1: Choosing what to model

Steve Brunton, University of Washington
University of Washington

AI/ML+Physics: Preview of Upcoming Modules and Bootcamps

Steve Brunton, University of Washington
University of Washington

High Level Overview of AI and ML in Science and Engineering

Steve Brunton, University of Washington
University of Washington

Discrepancy Modeling with Physics Informed Machine Learning

Steven L. Brunton, University of Washington