2024
Nonlinear model reduction for high- and low-consequence applications
Keynote at Model Reduction and Surrogate Modeling (MORe2024)
Challenges in AI for Science
Panel discussion at ICLR 2024 Workshop on AI4DifferentialEquations in Science alongside Max Welling and Shirley Ho
2020
Convolutional autoencoders and LSTMs: Using deep learning to overcome Kolmogorov-width limitations and accurately model errors in nonlinear model reduction
The explosion of artificial intelligence—especially techniques arising from deep neural networks—has yielded exciting …
2019
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
Nearly all model-reduction techniques project the governing equations onto a linear subspace of the original state space. Such …
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
Uncertainty-quantification tasks in computational physics are often “many query” in nature, as they require repeated evaluations of a …
Nonlinear reduced-order modeling: Using machine learning to enable extreme-scale simulation for many-query problems
Physics-based modeling and simulation has become indispensable across many applications in engineering and science, ranging from …
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
We propose a novel framework for projecting dynamical systems onto nonlinear trial manifolds using minimum-residual formulations at the …
Nonlinear model reduction: Using machine learning to enable extreme-scale simulation for many-query problems
Physics-based modeling and simulation has become indispensable across many applications in engineering and science, ranging from …
