Deep Ray

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Assad OberaiAME, USCE-mail confirmado em usc.edu
Jan S HesthavenProfessor of Mathematics and Provost, Ecole Polytechnique Federale de Lausanne (EPFL)E-mail confirmado em epfl.ch
Dhruv PatelPostdoctoral fellow, StanfordE-mail confirmado em stanford.edu
Siddhartha MishraProfessor of Applied Mathematics, ETH Zurich, SwitzerlandE-mail confirmado em sam.math.ethz.ch
Praveen ChandrashekarCenter for Applicable Mathematics, Tata Institute of Fundamental ResearchE-mail confirmado em tifrbng.res.in
Kjetil Olsen LyeSINTEFE-mail confirmado em sintef.no
Christian RohdeUniversität StuttgartE-mail confirmado em mathematik.uni-stuttgart.de
Qian WangBeijing Computational Science Research Center, EPFL, Tsinghua UniversityE-mail confirmado em csrc.ac.cn
Ulrik Skre FjordholmUniversity of OsloE-mail confirmado em math.uio.no
Jim MagieraIANS, University of StuttgartE-mail confirmado em mathematik.uni-stuttgart.de
Harisankar RamaswamyPhD student , University of Southern CaliforniaE-mail confirmado em usc.edu
Beatrice RiviereNoah G. Harding Chair and Prof. of Computational Applied Mathematics and Operations Research at RiceE-mail confirmado em rice.edu
Chen LiuDepartment of Mathematical Sciences, University of ArkansasE-mail confirmado em uark.edu
Ujjwal KoleyAssociate Professor, TIFR-Centre for Applicable Mathematics, BangaloreE-mail confirmado em math.tifrbng.res.in
Tanmay SarkarAssistant professor in IIT JammuE-mail confirmado em iitjammu.ac.in
Christopher ThieleGraduate Student, Rice UniversityE-mail confirmado em rice.edu
Tim De RyckETH ZürichE-mail confirmado em math.ethz.ch
Saeed MoazamiUniversity of Southern California (USC)E-mail confirmado em usc.edu

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Deep Ray

Assistant Professor, University of Maryland

E-mail confirmado em umd.edu - Página inicial

Deep Learning Conservation laws Uncertainty quantification CFD Pore-scale dynamics


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	186	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	83	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	81	2021
Constraint-aware neural networks for Riemann problems J Magiera, D Ray, JS Hesthaven, C Rohde Journal of Computational Physics 409, 109345, 2020	80	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	62	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	51	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	30	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	24	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
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	16	2022
Entropy stable schemes for compressible Euler equations D Ray, P Chandrashekar Int. J. Numer. Anal. Model. Ser. B 4 (4), 335-352, 2013	16	2013
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	14	2023
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	9	2024

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