An Efficient Non-Intrusive Uncertainty Propagation Method for Stochastic Multi-Physics Models
For researchers dealing with computationally expensive multi-physics models with high-dimensional uncertainty, this method offers a more efficient alternative to Monte-Carlo and standard spectral methods.
The paper presents a reduced non-intrusive spectral projection method for uncertainty propagation in stochastic multi-physics models, achieving computational gains over the standard NISP method by modularizing the task and constructing reduced approximations via linear algebra transformations.
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.