| Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem Q Wang, JS Hesthaven, D Ray Journal of computational physics 384, 289-307, 2019 | 290 | 2019 |
| Deep learning observables in computational fluid dynamics KO Lye, S Mishra, D Ray Journal of Computational Physics 410, 109339, 2020 | 187 | 2020 |
| An artificial neural network as a troubled-cell indicator D Ray, JS Hesthaven Submitted, 2017 | 187 | 2017 |
| Controlling oscillations in high-order discontinuous Galerkin schemes using artificial viscosity tuned by neural networks N Discacciati, JS Hesthaven, D Ray Journal of Computational Physics 409, 109304, 2020 | 93 | 2020 |
| Detecting troubled-cells on two-dimensional unstructured grids using a neural network D Ray, JS Hesthaven Journal of Computational Physics 397, 108845, 2019 | 81 | 2019 |
| Iterative surrogate model optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks KO Lye, S Mishra, D Ray, P Chandrashekar Computer Methods in Applied Mechanics and Engineering 374, 113575, 2021 | 79 | 2021 |
| Constraint-aware neural networks for Riemann problems J Magiera, D Ray, JS Hesthaven, C Rohde Journal of Computational Physics 409, 109345, 2020 | 79 | 2020 |
| Entropy stable scheme on two-dimensional unstructured grids for Euler equations D Ray, P Chandrashekar, US Fjordholm, S Mishra Communications in Computational Physics 19 (5), 1111-1140, 2016 | 64 | 2016 |
| Solution of physics-based Bayesian inverse problems with deep generative priors DV Patel, D Ray, AA Oberai Computer Methods in Applied Mechanics and Engineering 400, 115428, 2022 | 50 | 2022 |
| Controlling oscillations in spectral methods by local artificial viscosity governed by neural networks L Schwander, D Ray, JS Hesthaven Journal of Computational Physics 431, 110144, 2021 | 37 | 2021 |
| Variationally mimetic operator networks D Patel, D Ray, MRA Abdelmalik, TJR Hughes, AA Oberai Computer Methods in Applied Mechanics and Engineering 419, 116536, 2024 | 32 | 2024 |
| A sign preserving WENO reconstruction method US Fjordholm, D Ray Journal of Scientific Computing 68, 42-63, 2016 | 31 | 2016 |
| The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems D Ray, H Ramaswamy, DV Patel, AA Oberai arXiv preprint arXiv:2202.07773, 2022 | 26 | 2022 |
| An entropy stable finite volume scheme for the two dimensional Navier–Stokes equations on triangular grids D Ray, P Chandrashekar Applied Mathematics and Computation 314, 257-286, 2017 | 20 | 2017 |
| Multilevel Monte Carlo finite difference methods for fractional conservation laws with random data U Koley, D Ray, T Sarkar SIAM/ASA Journal on Uncertainty Quantification 9 (1), 65-105, 2021 | 18 | 2021 |
| Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty D Ray, J Murgoitio-Esandi, A Dasgupta, AA Oberai Computer Methods in Applied Mechanics and Engineering 417, 116338, 2023 | 15 | 2023 |
| A pressure-correction and bound-preserving discretization of the phase-field method for variable density two-phase flows C Liu, D Ray, C Thiele, L Lin, B Riviere Journal of Computational Physics 449, 110769, 2022 | 15 | 2022 |
| Entropy stable schemes for compressible Euler equations D Ray, P Chandrashekar Int. J. Numer. Anal. Model. Ser. B 4 (4), 335-352, 2013 | 15 | 2013 |
| On the approximation of rough functions with deep neural networks T De Ryck, S Mishra, D Ray SeMA Journal 79 (3), 399-440, 2022 | 11 | 2022 |
| A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows A Dasgupta, DV Patel, D Ray, EA Johnson, AA Oberai Computer Methods in Applied Mechanics and Engineering 420, 116682, 2024 | 10 | 2024 |
Assad OberaiAME, USCVerified email at usc.edu