Machine Learning, Dynamical Systems and Control

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Data-Driven Science and Engineering Seminar

University of Washington, Seattle

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


Mailing List: SIGN UP


Upcoming Talks


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November 16, 2023


Matthew Juniper
University of Cambridge


Title: Adjoint-accelerated Bayesian Inference



Previous Talks


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October 12, 2023 [VIEW]


Adarsh Krishnamurthy
Iowa State University


Title: Multi-scale Modeling and Simulations using Digital Twins



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May 5, 2023 [recording not available]


Christine Allen-Blanchette
Princeton University


Title: Learning Dynamics from Images Using Lagrangian/Hamiltonian Structure




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April 7, 2023 [ VIEW ]


Samy Wu Fong
Colorado School of Mines


Title: Using Hamilton-Jacobi PDEs for Optimization



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February 3, 2023 [ VIEW ]


David Bortz
University of Colorado Boulder


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



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January 6, 2023 [ VIEW ]


Tess Smidt


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



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December 2, 2022 [ VIEW ]


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


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


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