- On the approximation of rough functions with deep neural networks (T. De Ryck, S. Mishra, D. Ray);
*SeMA Journal, 2022.*[article] [report] - 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, Vol. 449, 2022.*[article] [report] - A discontinuous Galerkin method for a diffuse-interface model of immiscible two-phase flows with soluble surfactant (D. Ray, C. Liu, B. Riviere);
*Computational Geosciences, 2021.*[article] [report] - Controlling oscillations in spectral methods by local artificial viscosity governed by neural networks (L. Schwander, D. Ray, J. S. Hesthaven);
*Journal of Computational Physics, Vol. 431, 2021.*[article] [report] - Multi-level Monte Carlo finite difference methods for fractional conservation laws with random data (U. Koley, D. Ray, T. Sarkar);
*SIAM/ASA Journal on Uncertainty Quantification, Vol. 9(1), pp. 65-105, 2021.*[article] [report] - Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks (K. O. Lye, S. Mishra, D. Ray, P. Chandrashekar);
*Computer Methods in Applied Mechanics and Engineering, Vol. 374, 2021.*[article] [report] - Deep learning observables in computational fluid dynamics (K. O. Lye, S. Mishra, D. Ray);
*Journal of Computational Physics, Vol. 410, 2020*. [article] [report] - Constraint-Aware Neural Networks for Riemann Problems (J. Magiera, D. Ray, J. S. Hesthaven, C. Rohde);
*Journal of Computational Physics, Vol. 409, 2020.*[article] [report] - Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks (N. Discacciati, J. S. Hesthaven, D. Ray);
*Journal of Computational Physics, Vol. 409, 2020.*[article] [report] - Detecting troubled-cells on two-dimensional unstructured grids using a neural network (D. Ray, J. S. Hesthaven);
*Journal of Computational Physics, Vol. 397, 2019.*[article] [report] - Non-intrusive reduced order modelling of unsteady flows using artificial neural networks with application to a combustion problem (Q. Wang, J. S. Hesthaven, D.Ray);
*Journal of Computational Physics, Vol. 384, pp. 289-307, 2019.*[article] [report] - An artificial neural network as a troubled-cell indicator (D. Ray, J. S. Hesthaven);
*Journal of Computational Physics, Vol. 367 (15), pp. 166-191, 2018.*[article] [report] - An entropy stable finite volume scheme for the two dimensional Navierâ€“Stokes equations on triangular grids (D. Ray, P. Chandrashekar);
*Applied Mathematics and Computation, Vol. 314, pp. 257-286, 2017.*[article] - Convergence of fully discrete schemes for diffusive-dispersive conservation laws with discontinuous flux (U. Koley, R, Dutta, D. Ray);
*ESAIM: Mathematical Modelling and Numerical Analysis, Vol. 50(5), pp. 1289-1331, 2016.*[article] [report] - Entropy stable schemes on two-dimensional unstructured grids for Euler equations (D. Ray, P. Chandrashekar, U. Fjordholm, S. Mishra);
*Communications in Computational Physics, Vol. 19(5), pp. 1111-1140, 2016*[article] [report] - A sign preserving WENO reconstruction method (U. S. Fjordholm, D. Ray);
*Journal of Scientific Computing, Vol. 68(1), pp. 42-63, 2015.* [article] [report]

- Efficient posterior inference & generalization in physics-based Bayesian inference with conditional GANs (D. Ray, D. Patel, H. Ramaswamy, A. A. Oberai);
*NeurIPS Workshop on Deep Learning and Inverse Problems, 2021.*[article] - Bayesian Inference in Physics-Driven Problems with Adversarial Priors (D. Patel, D. Ray, H. Ramaswamy, A. A. Oberai);
*NeurIPS Workshop on Deep Learning and Inverse Problems, 2020.*[article] - A Third-Order Entropy Stable Scheme for the Compressible Euler Equations (D. Ray);
*In: Theory, Numerics and Applications of Hyperbolic Problems II. HYP 2016. Springer Proceedings in Mathematics and Statistics, vol. 237, 2018*[article] - Entropy stable schemes for compressible Euler equations (D. Ray, P. Chandrashekar);
*International Journal of Numerical Analysis and Modeling (Series B), 2013.*[article] - Kinetic energy preserving and entropy stable finite volume schemes for compressible Euler and Navier-Stokes equations (D. Ray, P. Chandrashekar);
*14â€™th AeSI CFD Symposium, IISc, Bangalore, 10-11 Aug, 2012.*

- Probabilistic Medical Image Imputation via Deep Adversarial Learning (R. Raad, D. Patel, C.-C. Hsu, V. Kothapalli, D. Ray, B. Varghese, D. Hwang, I. Gill, V. Duddalwar, A. A. Oberai);
*to appear in Engineering with Computers, 2022.* - Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks (S. Moazami, D. Ray, D. Pelletier, A. A. Oberai);
*submitted, 2022.*[report] - The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems (D. Ray, H. Ramaswamy, D. Patel, A. A. Oberai);
*submitted, 2022.*[report] - Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors (D. Patel, D. Ray, A. A. Oberai);
*submitted, 2021.*[report]

- Entropy-stable finite difference and finite volume schemes for compressible flows, 2017. [view]