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 …
