Pathwise Learning of Stochastic Dynamical Systems with Partial Observations

TL;DR

This paper introduces a neural path estimation method using variational inference to reconstruct and infer stochastic dynamical systems from noisy, partial observations, effectively handling complex data distributions.

Contribution

It develops a novel variational inference framework that learns to estimate posterior paths of stochastic systems directly from noisy observations.

Findings

Successfully applied to nonlinear stochastic systems

Handles multimodal data and chaotic dynamics

Performs well with sparse observations

Abstract

The reconstruction and inference of stochastic dynamical systems from data is a fundamental task in inverse problems and statistical learning. While surrogate modeling advances computational methods to approximate these dynamics, standard approaches typically require high-fidelity training data. In many practical settings, the data are indirectly observed through noisy and nonlinear measurement. The challenge lies not only in approximating the coefficients of the SDEs, but in simultaneously inferring the posterior updates given the observations. In this work, we present a neural path estimation approach to solve stochastic dynamical systems based on variational inference. We first derive a stochastic control problem that solve filtering posterior path measure corresponding to a pathwise Zakai equation. We then construct a generative model that maps the prior path measure to posterior measure through the controlled diffusion and the associated Randon-Nykodym derivative. Through an amortization of sample paths of the observation process, the control is learned through the noisy observation paths and we learn an associated SDE which induces the filtering path measure. In the end, we demonstrate the model's performance on various nonlinear stochastic systems, showcasing its ability to handle multimodal data distributions, chaotic dynamics, and sparse observation data.