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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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03/29/2023
A few-shot graph Laplacian-based approach for improving the accuracy of low-fidelity data
Low-fidelity data is typically inexpensive to generate but inaccurate. O...
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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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03/27/2020
GAN-based Priors for Quantifying Uncertainty
Bayesian inference is used extensively to quantify the uncertainty in an...
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09/13/2019
Spectral Analysis Of Weighted Laplacians Arising In Data Clustering
Graph Laplacians computed from weighted adjacency matrices are widely us...
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07/22/2019
