Structured Machine Learning and Time–Stepping for Dynamical Systems
Videos from BIRS Workshop
Lisa Kreusser, Univeristy of Bath
Monday Feb 19, 2024 10:59 - 11:44
Dynamical systems in deep generative modelling
Hongkun Zhang, University of Massachusetts Amherst
Monday Feb 19, 2024 13:29 - 14:15
Machine learning of conservation laws for dynamical systems
Christian Offen, Paderborn University
Monday Feb 19, 2024 14:17 - 15:00
Learning Lagrangian dynamics from data with UQ
Brynjulf Owren, NTNU
Monday Feb 19, 2024 15:28 - 16:09
Stability of numerical methods set on Euclidean spaces and manifolds with applications to neural networks
Simone Brugiapaglia, Concordia University
Monday Feb 19, 2024 16:11 - 17:02
Practical existence theorems for deep learning approximation in high dimensions
Davide Murari, NTNU
Tuesday Feb 20, 2024 09:00 - 09:43
Improving the robustness of Graph Neural Networks with coupled dynamical systems
Eldad Haber, UBC
Tuesday Feb 20, 2024 09:45 - 10:30
Time dependent graph neural networks
Geoffrey McGregor, University of Toronto
Tuesday Feb 20, 2024 13:34 - 14:12
Conservative Hamiltonian Monte Carlo
Wu Lin, Vector Institute
Tuesday Feb 20, 2024 14:16 - 14:58
(Lie-group) Structured Inverse-free Second-order Optimization for Large Neural Nets
Molei Tao, Georgia Institute of Technology
Tuesday Feb 20, 2024 15:32 - 16:17
Optimization and Sampling in Non-Euclidean Spaces
Melvin Leok, University of California, San Diego
Tuesday Feb 20, 2024 16:18 - 17:02
The Connections Between Discrete Geometric Mechanics, Information Geometry, Accelerated Optimization and Machine Learning
Elena Celledoni, Norwegian University of Science and Technology
Wednesday Feb 21, 2024 09:01 - 09:46
Deep neural networks on diffeomorphism groups for optimal shape reparameterization
Giacomo Dimarco, university of Ferrara
Wednesday Feb 21, 2024 09:47 - 10:29
Control and neural network uncertainty quantification for plasma simulation
Chris Budd, University of Bath
Thursday Feb 22, 2024 09:00 - 09:46
Adaptivity and expressivity in neural network approximations
Yen-Hsi Tsai, University of Texas at Austin
Thursday Feb 22, 2024 09:46 - 10:32
Efficient gradient descent algorithms for learning from multiscale data
Michael Graham, University of Wisconsin-Madison
Thursday Feb 22, 2024 11:01 - 11:48
Data-driven modeling of complex chaotic dynamics on invariant manifolds
Seth Taylor, McGill University
Thursday Feb 22, 2024 13:31 - 14:16
A spatiotemporal discretization for diffeomorphism approximation
Daisuke Furihata, Osaka University
Thursday Feb 22, 2024 15:31 - 16:07
A particle method based on Voronoi decomposition for the Cahn–Hilliard equation
David Ketcheson, King Abdullah University of Science and Technology
Thursday Feb 22, 2024 16:14 - 17:06
Explicit time discretizations that preserve dissipative or conservative energy dynamics
Kyriakos Flouris, ETH Zurich
Friday Feb 23, 2024 09:00 - 09:42
Geometry aware neural operators for hemodynamics
Yolanne Lee, UCL
Friday Feb 23, 2024 09:45 - 10:18
Learning PDEs from image data using invariant features
