A dynamical polynomial chaos approach for long-time evolution of SPDEs

JOURNAL OF COMPUTATIONAL PHYSICS

卷:343

Article

Ozen, H. Cagan¹  Bal, Guillaume¹ 

[1] Columbia Univ, Dept Appl Phys & Appl Math, New York, NY 10027 USA

关键词: Stochastic partial differential equations;    Polynomial chaos;    Karhunen-Loeve expansion;    Long time integration;   

DOI  :  10.1016/j.jcp.2017.04.054

来源: Elsevier

【 摘 要 】

We propose a Dynamical generalized Polynomial Chaos (DgPC) method to solve time-dependent stochastic partial differential equations (SPDEs) with white noise forcing. The long-time simulation of SPDE solutions by Polynomial Chaos (PC) methods is notoriously difficult as the dimension of the stochastic variables increases linearly with time. Exploiting the Markovian property of white noise, DgPC [1] implements a restart procedure that allows us to expand solutions at future times in terms of orthogonal polynomials of the measure describing the solution at a given time and the future white noise. The dimension of the representation is kept minimal by application of a Karhunen-Loeve (KL) expansion. Using frequent restarts and low degree polynomials on sparse multi-index sets, the method allows us to perform long time simulations, including the calculation of invariant measures for systems which possess one. We apply the method to the numerical simulation of stochastic Burgers and Navier-Stokes equations with white noise forcing. Our method also allows us to incorporate time-independent random coefficients such as a random viscosity. We propose several numerical simulations and show that the algorithm compares favorably with standard Monte Carlo methods. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

Free