Enabling Nonlinear Manifold Projection Reduced-Order Models by Extending Convolutional Neural Networks to Unstructured Data. - John Tencer; Kevin Potter (CoRR, 2020)

A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems. - Francesco Rizzi; Eric J. Parish; Patrick J. Blonigan; John Tencer (CoRR, 2020)

Parameterized Pseudo-Differential Operators for Graph Convolutional Neural Networks. - Kevin Potter; Steven Sleder; Matthew Smith; Shehan Perera; Alper Yilmaz; John Tencer (ICCVW, 2021)

A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations. - John Tencer; Kevin Potter (SIAM J. Sci. Comput., 2021)

Hyper-Reduced Autoencoders for Efficient and Accurate Nonlinear Model Reductions. - Jorio Cocola; John Tencer; Francesco Rizzi; Eric J. Parish; Patrick J. Blonigan (CoRR, 2023)

Interpretable and flexible non-intrusive reduced-order models using reproducing kernel Hilbert spaces. - Alejandro N. Diaz; Shane A McQuarrie; John Tencer; Patrick J. Blonigan (CoRR, 2025)