(Empirical) Gramian-based dimension reduction for stochastic differential equations driven by fractional Brownian motion

TL;DR

This paper develops and validates model reduction techniques for large-scale stochastic differential equations driven by fractional Brownian motion, addressing challenges in high-dimensional simulation and stability.

Contribution

It introduces empirical Gramians and projection-based reduced order models tailored for fractional Brownian motion driven systems, including special considerations for H=1/2.

Findings

Empirical Gramians can be learned from simulation data for fractional systems.

Reduced order models effectively approximate large-scale stochastic fractional PDEs.

Stability issues are identified and addressed for H=1/2 cases.

Abstract

In this paper, we investigate large-scale linear systems driven by a fractional Brownian motion (fBm) with Hurst parameter $H\in [1/2, 1)$. We interpret these equations either in the sense of Young ($H>1/2$) or Stratonovich ($H=1/2$). Especially fractional Young differential equations are well suited for modeling real-world phenomena as they capture memory effects. Although it is very complex to solve them in high dimensions, model reduction schemes for Young or Stratonovich settings have not yet been studied much. To address this gap, we analyze important features of fundamental solutions associated to the underlying systems. We prove a weak type of semigroup property which is the foundation of studying system Gramians. From the introduced Gramians, dominant subspace can be identified which is shown in this paper as well. The difficulty for fractional drivers with $H>1/2$ is that there is no link of the corresponding Gramians to algebraic equations making the computation very difficult. Therefore, we further propose empirical Gramians that can be learned from simulation data. Subsequently, we introduce projection-based reduced order models (ROMs) using the dominant subspace information. We point out that such projections are not always optimal for Stratonovich equations as stability might not be preserved and since the error might be larger than expected. Therefore, an improved ROM is proposed for $H=1/2$. We validate our techniques conducting numerical experiments on some large-scale stochastic differential equations driven by fBm resulting from spatial discretizations of fractional stochastic PDEs. Overall, our study provides useful insights into the applicability and effectiveness of reduced order methods for stochastic systems with fractional noise, which can potentially aid in the development of more efficient computational strategies for practical applications.