Machine Learning, Dynamical Systems and Control

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Data-Driven Methods for Science and Engineering Seminar

University of Washington, Seattle


Organizers: Joe Bakarji, Jason Bramburger, Henning Lange & Jordan Snyder
Faculty Organizers: Steven L. Brunton, J. Nathan Kutz & Krithika Manohar

 

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Next Talk

 


TO BE CONTINUED IN FALL 2021

 

 

Upcoming Talks and Schedule

 


TO BE CONTINUED IN FALL 2021

 

 

 

Previous Talks

 

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May 28, 2021 [ VIEW ]

 

Eva Kanso
University of Southern California

 

Title: One Fish, Two Fish

 

 

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May 14, 2021 [ VIEW ]

 

Nicholas Zabaras
Notre Dame

 

Title: Physics Informed Learning for Multiscale Dynamical Systems

 

 

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April 30, 2021

 

Rachel Ward
University of Texas, Austin

 

Title: Generalization bounds for sparse random feature expansions

 

 

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April 16, 2021

 

Dennice Gayme
Johns Hopkins University

 

Title: A new paradigm in wind farm modeling and control for power grid support

 

 

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April 9, 2021 [ VIEW ]

 

Kevin Carlberg
University of Washington

 

Title: AI for Computational Physics: Toward real-time high-fidelity simulation

 

 

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March 5, 2021 [ VIEW ]

 

Prof. Anima Anandkumar
California Institute of Technology

 

Title: Neural Operator for Parametric PDEs

 

 

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February 19, 2021 [ VIEW ]

 

Prof. Beverley McKeon
California Institute of Technology

 

Title: What's in a mean (what, how and why)? Towards nonlinear models of wall turbulence

 

 

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February 5, 2021 [ VIEW ]

 

Tamara Kolda
Sandia National Laboratories

 

Title: Practical Leveraged-Based Sampling for Low-Rank Tensor Decomposition

 

 

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January 22, 2021 [ VIEW ]

 

Prof. Zico Kolter
Carnegie Mellon University

 

Title: Incorporating physics and decision making into deep learning via implicit layers

 

 

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January 8, 2021 [ VIEW ]

 

Prof. Andrea Bertozzi
UCLA

 

Title: Total variation minimization on graphs for semisupervised and unsupervised machine learning

 

 

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December 11, 2020 [ VIEW ]

 

Prof. Cecilia Clementi
FU Berlin

 

Title: Designing molecular models by machine learning and experimental data

 

 

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November 13, 2020 [ VIEW ]

 

Prof. David Duvenaud
Vector Institute, University of Toronto

 

Title: Handling messy time series with large latent-variable models

 

 

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October 30, 2020 [ VIEW ]

 

Prof. Jeff Moehlis
Mechanical Engineering, UC Santa Barbara

 

Title: Learning to control population of neurons

 

 

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October 16, 2020 [ VIEW ]

 

Prof. Michael Mahoney
Statistics, Berkeley

 

Title: Dynamical systems and machine learning: combining in a principled way data-driven models and domain-driven models

 

 

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October 2, 2020 [ VIEW ]

 

Prof. George Em Karniadakis
Applied Mathematics, Brown University

 

Title: From PINNs to DeepOnets: Approximating functions, functionals, and operators using deep neural networks for diverse applications