A paradigm for data-driven predictive modeling using field inversion and machine learning EJ Parish, K Duraisamy Journal of computational physics 305, 758-774, 2016 | 588 | 2016 |
A priori estimation of memory effects in reduced-order models of nonlinear systems using the Mori–Zwanzig formalism A Gouasmi, EJ Parish, K Duraisamy Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2017 | 77* | 2017 |
Non-Markovian closure models for large eddy simulations using the Mori-Zwanzig formalism EJ Parish, K Duraisamy Phys. Rev. Fluids 2 (1), 014604, 2017 | 66 | 2017 |
A dynamic subgrid scale model for large eddy simulations based on the Mori–Zwanzig formalism EJ Parish, K Duraisamy Journal of Computational Physics 349, 154-175, 2017 | 64 | 2017 |
The Adjoint Petrov–Galerkin method for non-linear model reduction EJ Parish, CR Wentland, K Duraisamy Computer Methods in Applied Mechanics and Engineering 365, 112991, 2020 | 58* | 2020 |
Parameterized neural ordinary differential equations: Applications to computational physics problems K Lee, EJ Parish Proceedings of the Royal Society A 477 (2253), 20210162, 2021 | 53 | 2021 |
Time-series machine-learning error models for approximate solutions to parameterized dynamical systems EJ Parish, KT Carlberg Computer Methods in Applied Mechanics and Engineering 365, 112990, 2020 | 39 | 2020 |
A unified framework for multiscale modeling using the Mori-Zwanzig formalism and the variational multiscale method EJ Parish, K Duraisamy arXiv preprint arXiv:1712.09669, 2017 | 18 | 2017 |
Windowed least-squares model reduction for dynamical systems EJ Parish, KT Carlberg Journal of Computational Physics 426, 109939, 2021 | 17 | 2021 |
On the impact of dimensionally-consistent and physics-based inner products for POD-Galerkin and least-squares model reduction of compressible flows EJ Parish, F Rizzi Journal of Computational Physics 491, 112387, 2023 | 16 | 2023 |
Windowed space–time least-squares Petrov–Galerkin model order reduction for nonlinear dynamical systems YS Shimizu, EJ Parish Computer Methods in Applied Mechanics and Engineering 386, 114050, 2021 | 16* | 2021 |
Reduced order modeling of turbulent flows using statistical coarse-graining E Parish, K Duraisamy 46th AIAA Fluid Dynamics Conference, 3640, 2016 | 15 | 2016 |
Quantification of turbulence modeling uncertainties using full field inversion E Parish, K Duraisamy 22nd AIAA Computational Fluid Dynamics Conference, 2459, 2015 | 15 | 2015 |
Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems F Rizzi, PJ Blonigan, EJ Parish, KT Carlberg arXiv preprint arXiv:2003.07798, 2020 | 12 | 2020 |
Generalized Riemann problem-based upwind scheme for the vorticity transport equations E Parish, K Duraisamy, P Chandrashekar Computers & Fluids 132, 10-18, 2016 | 9 | 2016 |
Evaluation of dual-weighted residual and machine learning error estimation for projection-based reduced-order models of steady partial differential equations PJ Blonigan, EJ Parish Computer Methods in Applied Mechanics and Engineering 409, 115988, 2023 | 5 | 2023 |
Projection-based model reduction for coupled conduction—enclosure radiation systems V Brunini, EJ Parish, J Tencer, F Rizzi Journal of Heat Transfer 144 (6), 062101, 2022 | 5 | 2022 |
Turbulence modeling for compressible flows using discrepancy tensor-basis neural networks and extrapolation detection E Parish, DS Ching, NE Miller, SJ Beresh, MF Barone AIAA SciTech 2023 Forum, 2126, 2023 | 4 | 2023 |
Residual-based stabilized reduced-order models of the transient convection-diffusion-reaction equation obtained through discrete and continuous projection E Parish, M Yano, I Tezaur, T Iliescu arXiv preprint arXiv:2302.09355, 2023 | 3 | 2023 |
Uncertainty propagation of the negative Spallart–Allmaras turbulence model coefficients using projection-based reduced-order models EH Krath, PJ Blonigan, E Parish AIAA SCITECH 2023 Forum, 2041, 2023 | 3 | 2023 |