An Efficient Non-Intrusive Uncertainty Propagation Method for Stochastic Multi-Physics Models

TL;DR

This paper introduces a modular, non-intrusive spectral method for efficient uncertainty propagation in multi-physics models, significantly reducing computational costs compared to traditional spectral and Monte Carlo methods.

Contribution

It presents a reduced non-intrusive spectral projection approach that modularizes uncertainty propagation and employs data reduction techniques to lower computational expenses.

Findings

Achieves significant computational savings over standard NISP methods.

Effectively handles high-dimensional uncertain parameter spaces.

Demonstrates accuracy and efficiency through numerical examples.

Abstract

Multi-physics models governed by coupled partial differential equation (PDE) systems, are naturally suited for partitioned, or modular numerical solution strategies. Although widely used in tackling deterministic coupled models, several challenges arise in extending the benefits of modularization to uncertainty propagation. On one hand, Monte-Carlo (MC) based methods are prohibitively expensive as the cost of each deterministic PDE solve is usually quite large, while on the other hand, even if each module contains a moderate number of uncertain parameters, implementing spectral methods on the combined high-dimensional parameter space can be prohibitively expensive. In this work, we present a reduced non-intrusive spectral projection (NISP) based uncertainty propagation method which separates and modularizes the uncertainty propagation task in each subproblem using block Gauss-Seidel (BGS) techniques. The overall computational costs in the proposed method are also mitigated by constructing reduced approximations of the input data entering each module. These reduced approximations and the corresponding quadrature rules are constructed via simple linear algebra transformations. We describe these components of the proposed algorithm assuming a generalized polynomial chaos (gPC) model of the stochastic solutions. We demonstrate our proposed method and its computational gains over the standard NISP method using numerical examples.