167 citations · 426 across the 14 of their papers we have counts for
16 papers
A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows
Agnimitra Dasgupta, Dhruv V Patel, Deep Ray +2
We propose a novel modular inference approach combining two different generative models -- generative adversarial networks (GAN) and normalizing flows -- to approximate the posteri…
Learning end-to-end inversion of circular Radon transforms in the partial radial setup
Deep Ray, Souvik Roy
We present a deep learning-based computational algorithm for inversion of circular Radon transforms in the partial radial setup, arising in photoacoustic tomography. We first demon…
Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty
Deep Ray, Javier Murgoitio-Esandi, Agnimitra Dasgupta +1
The solution of probabilistic inverse problems for which the corresponding forward problem is constrained by physical principles is challenging. This is especially true if the dime…
Deep Learning and Computational Physics (Lecture Notes)
Deep Ray, Orazio Pinti, Assad A. Oberai
These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering gradua…
Variationally Mimetic Operator Networks
Dhruv Patel, Deep Ray, Michael R. A. Abdelmalik +2
In recent years operator networks have emerged as promising deep learning tools for approximating the solution to partial differential equations (PDEs). These networks map input fu…
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems
Deep Ray, Harisankar Ramaswamy, Dhruv V. Patel +1
In this work, we train conditional Wasserstein generative adversarial networks to effectively sample from the posterior of physics-based Bayesian inference problems. The generator…