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09/05/2023
Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
Increases in wildfire activity and the resulting impacts have prompted t...
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08/27/2023
Learning end-to-end inversion of circular Radon transforms in the partial radial setup
We present a deep learning-based computational algorithm for inversion o...
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06/08/2023
Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty
The solution of probabilistic inverse problems for which the correspondi...
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01/03/2023
Deep Learning and Computational Physics (Lecture Notes)
These notes were compiled as lecture notes for a course developed and ta...
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09/26/2022
Variationally Mimetic Operator Networks
Operator networks have emerged as promising deep learning tools for appr...
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02/15/2022
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems
In this work, we train conditional Wasserstein generative adversarial ne...
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07/06/2021
Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors
Inverse problems are notoriously difficult to solve because they can hav...
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10/30/2020
A pressure-correction and bound-preserving discretization of the phase-field method for variable density two-phase flows
In this paper, we present an efficient numerical algorithm for solving t...
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10/04/2020
A discontinuous Galerkin method for a diffuse-interface model of immiscible two-phase flows with soluble surfactant
A numerical method using discontinuous polynomial approximations is form...
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10/01/2020
Multi-level Monte Carlo Finite Difference Methods for Fractional Conservation Laws with Random Data
We establish a notion of random entropy solution for degenerate fraction...
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08/13/2020
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks
We present a novel active learning algorithm, termed as iterative surrog...
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12/13/2019
On the approximation of rough functions with deep neural networks
Deep neural networks and the ENO procedure are both efficient frameworks...
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04/29/2019
Constraint-Aware Neural Networks for Riemann Problems
Neural networks are increasingly used in complex (data-driven) simulatio...
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03/07/2019
