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.

Follow Steve Brunton:
On Twitter: @eigensteve
On YouTube: @Eigensteve

Speakers
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Prof. Steven L. Brunton
Prof. Steve Brunton
Prof. Steve Brunton
Pravinkumar Ghodake
Omid Z. Mehdizadeh PhD
Pacifique Mugisho
PhD Rodrigo Castellanos
Dr. Ana Larrañaga Janeiro
Vincent Zimmer
Mr Zain Ali BS (Hons.) Physics
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Brunton Lab
Brunton Lab
Steve Brunton (Eigensteve)
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Steve Brunton
University of Washington
University of Washington
Host
Speaker
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Steven L. Brunton
University of Washington
 
University of Washington
Speaker
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Pravinkumar Ghodake
Indian Institute of Technology Bombay
Indian Institute of Technology Bombay
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Pacifique Mugisho
University of Johannesburg
University of Johannesburg
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Rodrigo Castellanos
Universidad Carlos III de Madrid
Universidad Carlos III de Madrid
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Ana Larrañaga Janeiro
AI Institute in Dynamic Systems
University of Washington
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Vincent Zimmer
Independent
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Zain Ali
University of the Punjab
 
University of the Punjab
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Omid Z. Mehdizadeh
University of Toronto Alumnus
 
Marc Montagnac
ISAE-SUPAERO
Cheng Long
Huawei Technologies (China)
Huawei Technologies (China)
Kutand Alkım Bayer
Izmir Institute of Technology
Izmir Institute of Technology
Seo Yoon JUNG
Korea Atomic Energy Research Institute
Korea Atomic Energy Research Institute
Aritra Kumar Mukhopadhyay
Technical University of Darmstadt
Technical University of Darmstadt
Marko Herkaliuk
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”