Machine Learning, Dynamical Systems and Control

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Data-Driven Science and Engineering Seminar

University of Washington, Seattle

 

Organizers: Joe Bakarji, Ryan Raut & Zack Nicolaou
Faculty Organizers: Steven L. Brunton, J. Nathan Kutz & Krithika Manohar

 

Mailing List: [ SIGN UP ]

 

Upcoming Talks

 

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February 3, 2023

 

David Bortz
University of Colorado Boulder

 

Title: The Surprising Robustness and Computational Efficiency of Weak Form System Identification

 

 

Previous Talks

 

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January 6, 2023

 

Tess Smidt
MIT

 

Title: Double Dipping: Problems and solutions, with applications to single-cell RNA-sequencing data

 

 

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December 2, 2022

 

Daniela Witten
University of Washington

 

Title: Double Dipping: Problems and solutions, with applications to single-cell RNA-sequencing data

 

 

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November 4, 2022 [VIEW ]

 

Melanie Weber
Harvard University

 

Title: Exploiting Geometric Structure in Machine Learning and Optimization

 

 

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October 7, 2022 [VIEW ]

 

John Wright
Columbia University

 

Title: Deep Networks and the Multiple Manifold Problem

 

 

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June 3, 2022 [VIEW ]

 

Jorn Dunkel
MIT

 

Title: Symmetry-informed model inference for active matter

 

 

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May 6, 2022 [VIEW ]

 

Andrew Stuart
California Institute of Technology

 

Title: Supervised Learning for Operators

 

 

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April 1, 2022 [VIEW ]

 

Rich Baraniuk
Rice University

 

Title: Deep Network Spline Geometry

 

 

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March 4, 2022 [VIEW ]

 

Liliana Borcea
University of Michigan

 

Title: Waveform inversion via reduced order modeling

 

 

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February 4, 2022 [VIEW ]

 

Laure Zanna
Courant Institute for Mathematical Sciences, NYU

 

Title: Data-driven turbulence closures for ocean and climate models: advances and challenges

 

 

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December 3, 2021 [ VIEW ]

 

Joan Bruna Estrach
Courant Institute of Mathematical Sciences, NYU

 

Title: Prospects and Challenges of Machine Learning in the Physical World

 

 

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November 19, 2021 [ VIEW ]

 

Benjamin Peherstorfer
Courant Institute of Mathematical Sciences, NYU

 

Title: Physics-based machine learning for quickly simulating transport-dominated physical phenomena

 

 

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November 12, 2021 [ VIEW ]

 

Jane Bae
California Institute of Technology

 

Title: Wall-models of turbulent flows via scientific multi-agent reinforcement learning

 

 

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November 5, 2021 [ VIEW ]

 

Rose Yu
University of California, San Diego

 

Title: Incorporating symmetry for learning spatiotemporal dynamics

 

 

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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
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