Publications and reports

Journals

  1. On the approximation of rough functions with deep neural networks (T. De Ryck, S. Mishra, D. Ray); SeMA Journal, 2022. [article] [report]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. Deep learning observables in computational fluid dynamics (K. O. Lye, S. Mishra, D. Ray); Journal of Computational Physics, Vol. 410, 2020. [article] [report]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. An entropy stable finite volume scheme for the two dimensional Navier–Stokes equations on triangular grids (D. Ray, P. Chandrashekar); Applied Mathematics and Computation, Vol. 314, pp. 257-286, 2017. [article]
  14. 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]
  15. 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]
  16. A sign preserving WENO reconstruction method (U. S. Fjordholm, D. Ray); Journal of Scientific Computing, Vol. 68(1), pp. 42-63, 2015.
  17. [article] [report]

Conference proceedings and workshops

  1. 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]
  2. 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]
  3. 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]
  4. Entropy stable schemes for compressible Euler equations (D. Ray, P. Chandrashekar); International Journal of Numerical Analysis and Modeling (Series B), 2013. [article]
  5. Kinetic energy preserving and entropy stable finite volume schemes for compressible Euler and Navier-Stokes equations (D. Ray, P. Chandrashekar); 14’th AeSI CFD Symposium, IISc, Bangalore, 10-11 Aug, 2012.

Submitted for publication

  1. 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.
  2. Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks (S. Moazami, D. Ray, D. Pelletier, A. A. Oberai); submitted, 2022. [report]
  3. 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]
  4. Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors (D. Patel, D. Ray, A. A. Oberai); submitted, 2021. [report]

Doctoral thesis

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