21 citations · 25 across the 12 of their papers we have counts for
6 papers · 1 filter
Conditional flow matching for physics-constrained inverse problems with finite training data
Agnimitra Dasgupta, Ali Fardisi, Mehrnegar Aminy +4
This study presents a conditional flow matching framework for solving physics-constrained Bayesian inverse problems. In this setting, samples from the joint distribution of inferre…
Closed-form conditional diffusion models for data assimilation
Brianna Binder, Agnimitra Dasgupta, Assad Oberai
We propose closed-form conditional diffusion models for data assimilation. Diffusion models use data to learn the score function (defined as the gradient of the log-probability den…
Time-dependent density estimation using binary classifiers
Agnimitra Dasgupta, Javier Murgoitio-Esandi, Ali Fardisi +1
We propose a data-driven method to learn the time-dependent probability density of a multivariate stochastic process from sample paths, assuming that the initial probability densit…
Conditional score-based diffusion models for solving inverse problems in mechanics
Agnimitra Dasgupta, Harisankar Ramaswamy, Javier Murgoitio-Esandi +5
We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a s…
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…
Uncertainty quantification for ptychography using normalizing flows
Agnimitra Dasgupta, Zichao Wendy Di
Ptychography, as an essential tool for high-resolution and nondestructive material characterization, presents a challenging large-scale nonlinear and non-convex inverse problem; ho…