Parameter Estimation in Stochastic Differential Equations via Wiener Chaos Expansion and Stochastic Gradient Descent

TL;DR

This paper introduces a novel method combining Wiener Chaos Expansion with Stochastic Gradient Descent to efficiently estimate parameters in SDEs, reducing computational costs and improving accuracy.

Contribution

It presents a new spectral decomposition approach that transforms stochastic inference into a deterministic optimization problem, enhancing scalability and robustness.

Findings

Accurate parameter recovery from noisy, discrete data

Significant reduction in computational burden compared to traditional methods

Effective modeling of complex non-linear SDEs, including biological growth models

Abstract

This study addresses the inverse problem of parameter estimation for Stochastic Differential Equations (SDEs) by minimizing a regularized discrepancy functional via Stochastic Gradient Descent (SGD). To achieve computational efficiency, we leverage the Wiener Chaos Expansion (WCE), a spectral decomposition technique that projects the stochastic solution onto an orthogonal basis of Hermite polynomials. This transformation effectively maps the stochastic dynamics into a hierarchical system of deterministic functions, termed the \textit{propagator}. By reducing the stochastic inference task to a deterministic optimization problem, our framework circumvents the heavy computational burden and sampling requirements of traditional simulation-based methods like MCMC or MLE. The robustness and scalability of the proposed approach are demonstrated through numerical experiments on various non-linear SDEs, including models for individual biological growth. Results show that the WCE-SGD framework provides accurate parameter recovery even from discrete, noisy observations, offering a significant paradigm shift in the efficient modeling of complex stochastic systems.