Variational Smoothing and Inference for SDEs from Sparse Data with Dynamic Neural Flows

ArXi:2605.05606v1 Announce Type: cross Stochastic differential equations (SDEs) provide a flexible framework for modeling temporal dynamics in partially observed systems. A central task is to calibrate such models from data, which requires inferring latent trajectories and parameters from sparse, noisy observations. Classical smoothing methods for this problem are often limited by path degeneracy and poor scalability.