Books

  • Deep Learning and Computational Physics
    D. Ray, O. Pinti, A. A. Oberai
    Springer Nature Switzerland, 2024. [springer link]

Journal Publications


Bayesian inference using generative machine learning
  1. Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts. (B. Shaddy, D. Ray, A. Farguell, V. Calaza, J. Mandel, J. Haley, K. Hilburn, D. V. Mallia, A. Kochanski, A. Oberai); Artificial Intelligence for Earth Systems [article] [preprint]
  2. A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows (A. Dasgupta, D. V. Patel, D. Ray, E. A. Johnson, A. A. Oberai); Computer Methods in Applied Mechanics and Engineering, Vol. 420, 2024. [article] [preprint]
  3. Conditional Generative Learning for Medical Image Imputation (R. Raad, D. Ray, B. Varghese, D. Hwang, I. Gill, V. Duddalwar, A. A. Oberai); Scientfic Reports 14, 171, 2024. [preprint]
  4. The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems (D. Ray, H. Ramaswamy, D. Patel, A. A. Oberai); Numerical Algebra, Control and Optimization, Vol. 14(1), 160-189, 2024. [article] [preprint]
  5. Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks (S. Moazami, D. Ray, D. Pelletier, A. A. Oberai); IEEE Transactions on Medical Imaging, 43(3), 1071-1088, 2024. [article] [preprint]
  6. Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty (D. Ray, J. Murgoitio-Esandi, A. Dasgupta, A. A. Oberai); Computer Methods in Applied Mechanics and Engineering, Vol. 417, 2023. [article] [preprint]
  7. Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors (D. Patel, D. Ray, A. A. Oberai); Computer Methods in Applied Mechanics and Engineering, Vol. 400, 2022. [article] [preprint]
  8. 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); Engineering with Computers, 2022. [article]
Learning surrogates and reduced order models
  1. Variationally Mimetic Operator Networks (D. Patel, D. Ray, M. R. A. Abdelmalik, T. J. R. Hughes, A. A. Oberai); Computer Methods in Applied Mechanics and Engineering, Vol. 419, 2024. [article] [preprint]
  2. Fourier Collocation and Reduced Basis Methods for Fast Modeling of Compressible Flows (J. Yu, D. Ray, J. S. Hesthaven); Communications in Computational Physics, 32 (3), 595-637, 2022. [article]
  3. 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] [preprint]
  4. Deep learning observables in computational fluid dynamics (K. O. Lye, S. Mishra, D. Ray); Journal of Computational Physics, Vol. 410, 2020. [article] [preprint]
  5. 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] [preprint]
Deep learning-guided shock capturing for high-order algorithms
  1. On the approximation of rough functions with deep neural networks (T. De Ryck, S. Mishra, D. Ray); SeMA Journal, 2022. [article] [preprint]
  2. 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] [preprint]
  3. 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] [preprint]
  4. Constraint-Aware Neural Networks for Riemann Problems (J. Magiera, D. Ray, J. S. Hesthaven, C. Rohde); Journal of Computational Physics, Vol. 409, 2020. [article] [preprint]
  5. 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] [preprint]
  6. 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] [preprint]
High-order solvers for conservation laws
  1. 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] [preprint]
  2. 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]
  3. 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] [preprint]
  4. 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] [preprint]
  5. A sign preserving WENO reconstruction method (U. S. Fjordholm, D. Ray); Journal of Scientific Computing, Vol. 68(1), pp. 42-63, 2015. [article] [preprint]
Multi-phase flow through porous structures
  1. 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] [preprint]
  2. 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] [preprint]

Conference proceedings and workshops

  1. Conditional score-based generative models for solving physics-based inverse problems. (A. Dasgupta, J. Murgoitio-Esandi, D. Ray, A. A. Oberai.); NeurIPS Workshop on Deep Learning and Inverse Problems, 2023. [article]
  2. GAN-Flow: A dimension-reduced variational framework for physics-based inverse problems. (A. Dasgupta, D. Patel, D. Ray, E. Johnson, A. A. Oberai.); NeurIPS Workshop on Machine Learning and the Physical Sciences, 2022. [article]
  3. 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]
  4. 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]
  5. 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]
  6. Entropy stable schemes for compressible Euler equations (D. Ray, P. Chandrashekar); International Journal of Numerical Analysis and Modeling (Series B), 2013. [article]
  7. 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.

Submissions under review

  1. Learning WENO for entropy stable schemes to solve conservation laws (P. Charles, D. Ray); submitted, 2024. [preprint]
  2. Learning end-to-end inversion of circular Radon transforms in the partial radial setup (D. Ray, S. Roy); submitted, 2023. [preprint]

Doctoral thesis

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