Dynamical polynomial chaos expansions and long time evolution of differential equations with random forcing

TL;DR

This paper introduces a Dynamical generalized Polynomial Chaos (DgPC) method that efficiently propagates uncertainties in stochastic differential equations over long times, reducing computational costs and capturing long-term behavior.

Contribution

It proposes a novel DgPC framework with a restart procedure that maintains low stochastic dimension and provides convergence analysis for long-term solutions.

Findings

DgPC accurately captures long-time dynamics of SDEs.

The method effectively approximates invariant measures.

Numerical results confirm theoretical convergence.

Abstract

Polynomial chaos expansions (PCE) allow us to propagate uncertainties in the coefficients of differential equations to the statistics of their solutions. Their main advantage is that they replace stochastic equations by systems of deterministic equations. Their main challenge is that the computational cost becomes prohibitive when the dimension of the parameters modeling the stochasticity is even moderately large. We propose a generalization of the PCE framework that allows us to keep this dimension as small as possible in favorable situations. For instance, in the setting of stochastic differential equations (SDEs) with Markov random forcing, we expect the future evolution to depend on the present solution and the future stochastic variables. We present a restart procedure that precisely allows PCE to depend only on that information. The computational difficulty then becomes the construction of orthogonal polynomials for dynamically evolving measures. We present theoretical results of convergence for our Dynamical generalized Polynomial Chaos (DgPC) method. Numerical simulations for linear and nonlinear SDEs show that it adequately captures the long-time behavior of their solutions as well as their invariant measures when the latter exist.