20 citations · 24 across the 5 of their papers we have counts for
5 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…
Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
Bryan Shaddy, Deep Ray, Angel Farguell +7
Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in u…
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…
A few-shot graph Laplacian-based approach for improving the accuracy of low-fidelity data
Orazio Pinti, Assad A. Oberai
Low-fidelity data is typically inexpensive to generate but inaccurate. On the other hand, high-fidelity data is accurate but expensive to obtain. Multi-fidelity methods use a small…
A new class of finite element variational multiscale turbulence models for incompressible magnetohydrodynamics
David Sondak, John N. Shadid, Assad A. Oberai +3
New large eddy simulation (LES) turbulence models for incompressible magnetohydrodynamics (MHD) derived from the variational multiscale (VMS) formulation for finite element simulat…