A paradigm for data-driven predictive modeling using field inversion and machine learning. - Eric J. Parish; Karthik Duraisamy (J. Comput. Phys., 2016)

A dynamic subgrid scale model for Large Eddy Simulations based on the Mori-Zwanzig formalism. - Eric J. Parish; Karthik Duraisamy (J. Comput. Phys., 2017)

Time-series machine-learning error models for approximate solutions to parameterized dynamical systems. - Eric J. Parish; Kevin T. Carlberg (CoRR, 2019)

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 Neural Ordinary Differential Equations: Applications to Computational Physics Problems. - Kookjin Lee; Eric J. Parish (CoRR, 2020)

Windowed space-time least-squares Petrov-Galerkin method for nonlinear model order reduction. - Yukiko S. Shimizu; Eric J. Parish (CoRR, 2020)

Space-time reduced-order modeling for uncertainty quantification. - Ruhui Jin; Francesco Rizzi; Eric J. Parish (CoRR, 2021)

Windowed least-squares model reduction for dynamical systems. - Eric J. Parish; Kevin T. Carlberg (J. Comput. Phys., 2021)

Residual-based stabilized reduced-order models of the transient convection-diffusion-reaction equation obtained through discrete and continuous projection. - Eric J. Parish; Masayuki Yano; Irina Tezaur; Traian Iliescu (CoRR, 2023)

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

On the impact of dimensionally-consistent and physics-based inner products for POD-Galerkin and least-squares model reduction of compressible flows. - Eric J. Parish; Francesco Rizzi (J. Comput. Phys., 2023)