K. Garikipati

Learning Population-Level Dynamics through a Latent Fokker-Planck Model and Discrepancy Transport Maps

This work presents a population-level inference framework that recovers latent stochastic dynamics directly from snapshot probability distributions by decomposing the observed evolution into an intrinsic latent stochastic process and a discrepancy transport map that captures geometric deformation between the latent and observed probability spaces.