3 papers
stat.ML2026
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…
stat.ML2025
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…
cs.LG2025
Unifying and extending Diffusion Models through PDEs for solving Inverse Problems
Agnimitra Dasgupta, Alexsander Marciano da Cunha, Ali Fardisi +4
Diffusion models have emerged as powerful generative tools with applications in computer vision and scientific machine learning (SciML), where they have been used to solve large-sc…