4 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…