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
Faculty Organizers: Steven L. Brunton, J. Nathan Kutz & Krithika Manohar
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Next Talk
November 12, 2021
Jane Bae
California Institute of Technology
California Institute of Technology
Title: Wall-models of turbulent flows via scientific multi-agent reinforcement learning
Upcoming Talks and Schedule
November 19, 2021: Benjamine Peherstorfer, Courant Institute NYU
December 3, 2021: Joan Bruna Estrach, Courant Institute, NYU
Previous Talks
November 5, 2021
Rose Yu
University of California, San Diego
University of California, San Diego
Title: Incorporating symmetry for learning spatiotemporal dynamics
May 14, 2021 [ VIEW ]
Nicholas Zabaras
Notre Dame
Notre Dame
Title: Physics Informed Learning for Multiscale Dynamical Systems
April 30, 2021
Rachel Ward
University of Texas, Austin
University of Texas, Austin
Title: Generalization bounds for sparse random feature expansions
April 16, 2021
Dennice Gayme
Johns Hopkins University
Johns Hopkins University
Title: A new paradigm in wind farm modeling and control for power grid support
April 9, 2021 [ VIEW ]
Kevin Carlberg
University of Washington
University of Washington
Title: AI for Computational Physics: Toward real-time high-fidelity simulation
March 5, 2021 [ VIEW ]
Prof. Anima Anandkumar
California Institute of Technology
California Institute of Technology
Title: Neural Operator for Parametric PDEs
February 19, 2021 [ VIEW ]
Prof. Beverley McKeon
California Institute of Technology
California Institute of Technology
Title: What's in a mean (what, how and why)? Towards nonlinear models of wall turbulence
February 5, 2021 [ VIEW ]
Tamara Kolda
Sandia National Laboratories
Sandia National Laboratories
Title: Practical Leveraged-Based Sampling for Low-Rank Tensor Decomposition
January 22, 2021 [ VIEW ]
Prof. Zico Kolter
Carnegie Mellon University
Carnegie Mellon University
Title: Incorporating physics and decision making into deep learning via implicit layers
January 8, 2021 [ VIEW ]
Prof. Andrea Bertozzi
UCLA
UCLA
Title: Total variation minimization on graphs for semisupervised and unsupervised machine learning
December 11, 2020 [ VIEW ]
Prof. Cecilia Clementi
FU Berlin
FU Berlin
Title: Designing molecular models by machine learning and experimental data
November 13, 2020 [ VIEW ]
Prof. David Duvenaud
Vector Institute, University of Toronto
Vector Institute, University of Toronto
Title: Handling messy time series with large latent-variable models
October 30, 2020 [ VIEW ]
Prof. Jeff Moehlis
Mechanical Engineering, UC Santa Barbara
Mechanical Engineering, UC Santa Barbara
Title: Learning to control population of neurons
October 16, 2020 [ VIEW ]
Prof. Michael Mahoney
Statistics, Berkeley
Statistics, Berkeley
Title: Dynamical systems and machine learning: combining in a principled way data-driven models and domain-driven models
October 2, 2020 [ VIEW ]
Prof. George Em Karniadakis
Applied Mathematics, Brown University
Applied Mathematics, Brown University
Title: From PINNs to DeepOnets: Approximating functions, functionals, and operators using deep neural networks for diverse applications