Deep Ray
Cited by
Cited by
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
An artificial neural network as a troubled-cell indicator
D Ray, JS Hesthaven
Submitted, 2017
Deep learning observables in computational fluid dynamics
KO Lye, S Mishra, D Ray
Journal of Computational Physics 410, 109339, 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
Detecting troubled-cells on two-dimensional unstructured grids using a neural network
D Ray, JS Hesthaven
Journal of Computational Physics 397, 108845, 2019
A sign preserving WENO reconstruction method
US Fjordholm, D Ray
Journal of Scientific Computing 68 (1), 42-63, 2016
Constraint-aware neural networks for Riemann problems
J Magiera, D Ray, JS Hesthaven, C Rohde
Journal of Computational Physics 409, 109345, 2020
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
Entropy stable schemes for compressible Euler equations
D Ray, P Chandrashekar
Int. J. Numer. Anal. Model. Ser. B 4 (4), 335-352, 2013
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
Controlling oscillations in high-order schemes using neural networks
N Discacciati
A Third-Order Entropy Stable Scheme for the Compressible Euler Equations
D Ray
XVI International Conference on Hyperbolic Problems: Theory, Numerics …, 2016
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
Multi-level Monte Carlo finite difference methods for fractional conservation laws with random data
U Koley, D Ray, T Sarkar
arXiv preprint arXiv:2010.00537, 2020
On the approximation of rough functions with deep neural networks
T De Ryck, S Mishra, D Ray
arXiv preprint arXiv:1912.06732, 2019
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
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
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
arXiv preprint arXiv:2010.16044, 2020
A discontinuous Galerkin method for a diffuse-interface model of immiscible two-phase flows with soluble surfactant
D Ray, C Liu, B Riviere
arXiv preprint arXiv:2010.01661, 2020
MATHICSE Technical Report: Constraint-Aware Neural Networks for Riemann Problems
J Magiera, D Ray, JS Hesthaven, C Rohde
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