Maria K. Cameron

University of Maryland, Department of Mathematics

Brin Mathematics Research Center Workshop

Scientific Machine Learning: Theory and Algorithms", Feb. 21, 2024 -- Feb. 23, 2024

Speakers and Slides

Wednesday, Feb. 21

• Reza Malek-Madani (Office of Naval Research, USA): Scientific Machine Learning and Mathematical Analysis. ONR and DoD Investments.
• Jinchao Xu (KAUST, Saudi Arabia): Deep Neural Networks and Finite Elements
• Ramani Duraiswami (University of Maryland, USA): Making Scientific Computing Models Differentiable for Deep Learning
• Chunmei Wang (University of Florida): Pseudo-differential Integral Autoencoder Network for Inverse PDE Operators
• Yuehaw Khoo (University of Chicago, USA): High-dimensional PDEs, tensor-network, and convex optimization
• Yunan Yang (Cornell University, USA): Neural Inverse Operators for Solving Inverse Problems in PDEs
• Ke Chen (University of Maryland, USA): Towards efficient deep operator learning for forward and inverse PDEs: theory and algorithms
• Samuel Lanthaler (California Institute of Technology, USA): Data-Complexity bounds for operator learning

Thursday, Feb. 22

• Mauro Maggioni (John Hopkins University, USA): Learning Interaction laws in particle- and agent-based systems
• James Murphy (Tufts University, USA): Intrinsic models in Wasserstein space with applications in molecular dynamics
• Rebecca Willet (University of Chicago, USA): Deep Stochastic Mechanics
• Tom Hickling (University of Oxford, UK): Adjoint Optimization of Deep-Learning Sub-Grid Scale Models for Large Eddy Simulation of Compressible Flows
• Boris Hanin (Princeton University, USA): Principled Hyperparameter Transfer Across Depth and Width in Neural Network
• Jiequn Han (Flatiron Institute, USA): A Neural Network Warm-Start Approach for Inverse Scattering Problems
• Ling Liang (University of Maryland, USA): On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
• Zezheng Song: (University of Maryland, USA): A finite-expression method for solving high-dimensional committor problems
• Shashank Sule (University of Maryland, USA): Sharp error estimates for target-measure diffusion maps
• Margot Yuan: (University of Maryland, USA): Optimal control for sampling the transition path process and estimating rates

Friday, February 23

• Jianfeng Lu (Duke University, USA): Representations of symmetric and antisymmetric functions
• Alex Townsend (Cornell University, USA): Elliptic PDE learning is provably data-efficient
• Deep Ray (University of Maryland, USA): Learning WENO for entropy stable schemes to solve conservation laws
• Holden Lee (John Hopkins University, USA): Theoretical foundations for diffusion models

Design by Michelle Cameron