Hyper-Reduced Autoencoders for Efficient and Accurate Nonlinear Model Reductions.
CoRR, 2023
A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations.
SIAM J. Sci. Comput., 2021
Parameterized Pseudo-Differential Operators for Graph Convolutional Neural Networks.
Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021
A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems.
CoRR, 2020
Enabling Nonlinear Manifold Projection Reduced-Order Models by Extending Convolutional Neural Networks to Unstructured Data.
CoRR, 2020