2023 Wilmington NC USA
Special Session 73: Data-driven methods in dynamical systems
Organizer(s): Ruhui Jin , Shi Chen , Qin Li
Parallel Session 3 :: Thursday, June 1, 08:00 – 09:30 MO209
8:00-8:30
Longxiu Huang
(Michigan State University, USA)
Bandlimited Graph Signal Recovery from Randomized Space-time Samples
8:30-9:00
Yifan Chen
(Caltech, USA)
Gradient flows for sampling: affine invariance and numerical approximations
9:00-9:30
Charles Kulick
(University of California, Santa Barbara, USA)
Scalable Multi-Species Agent-Based Modeling with Sparse GP
Parallel Session 4 :: Thursday, June 1, 14:00 – 16:00 MO209
14:00-14:30
Victor Churchill
(The Ohio State University, USA)
Learning the Evolution of Unknown Systems via Deep Neural Networks
14:30-15:00
Changhong Mou
(University of Wisconsin-Madison, USA)
Combining Stochastic Parameterized Reduced-Order Models with Machine Learning for Data Assimilation and Uncertainty Quantification with Partial Observations
15:00-15:30
Yinling Zhang
(University of Wisconsin Madison, USA)
A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization
15:30-16:00
Tulin Kaman
(University of Arkansas, USA)
Advancements in reduced order modeling and physics-informed neural networks for solving large scale partial differential equations
Parallel Session 5 :: Thursday, June 1, 16:30 – 19:00 MO209
16:30-17:00
Ke Chen
(University of Maryland at College Park, USA)
Deep Operator Learning Lessens the Curse of Dimensionality for PDEs
17:00-17:30
Shi Chen
(University of Wisconsin-Madison, USA)
Global Convergence of Gradient Descent for Multi-Layer ResNets with Homogeneous Activation Functions in the Mean-Field Regime
17:30-18:00
Shukai Du
(University of Wisconsin-Madison, USA)
Fast, low-memory methods for radiative transfer via hp-adaptive mesh refinement
18:00-18:30
Ruhui Jin
(UW-Madison, USA)
Tensor-structured sketching for constrained optimization
