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Found 10 papers, 1 papers with code
Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty
The cWGAN developed in this work differs from earlier versions in that its critic is required to be 1-Lipschitz with respect to both the inferred and the measurement vectors and not just the former.
A few-shot graph Laplacian-based approach for improving the accuracy of low-fidelity data
In the approach described in this paper, this is accomplished by constructing a graph Laplacian using the low-fidelity data and computing its low-lying spectrum.
Deep Learning and Computational Physics (Lecture Notes)
These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California.
Variationally Mimetic Operator Networks
The application of the VarMiON to a canonical elliptic PDE and a nonlinear PDE reveals that for approximately the same number of network parameters, on average the VarMiON incurs smaller errors than a standard DeepONet and a recently proposed multiple-input operator network (MIONet).
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems
In this work, we train conditional Wasserstein generative adversarial networks to effectively sample from the posterior of physics-based Bayesian inference problems.
GAN-based Priors for Quantifying Uncertainty
Bayesian inference is used extensively to quantify the uncertainty in an inferred field given the measurement of a related field when the two are linked by a mathematical model.
Quantifying uncertainty with GAN-based priors
Bayesian inference is used extensively to quantify the uncertainty in an inferred field given the measurement of a related field when the two are linked by a mathematical model.
GAN priors for Bayesian inference
Bayesian inference is used extensively to infer and to quantify the uncertainty in a field of interest from a measurement of a related field when the two are linked by a mathematical model.
Spectral Analysis Of Weighted Laplacians Arising In Data Clustering
Graph Laplacians computed from weighted adjacency matrices are widely used to identify geometric structure in data, and clusters in particular; their spectral properties play a central role in a number of unsupervised and semi-supervised learning algorithms.
Bayesian Inference with Generative Adversarial Network Priors
In this work we demonstrate how this approximate distribution may be used as a prior in a Bayesian update, and how it addresses the challenges associated with characterizing complex prior distributions and the large dimension of the inferred field.
