Efficient uncertainty propagation for network multiphysics systems

TL;DR

This paper introduces an efficient, structure-exploiting polynomial surrogate modeling approach for uncertainty quantification in network-coupled multiphysics systems, significantly reducing computational costs.

Contribution

It presents an intrusive method that leverages the composite function structure of multiphysics systems to construct reduced polynomial bases, enabling faster UQ computations.

Findings

Substantial computational savings demonstrated on a nuclear reactor model

Reduced polynomial basis depends on coupling terms, not full uncertainty space

Method effectively exploits system structure for efficient uncertainty propagation

Abstract

We consider a multiphysics system with multiple component models coupled together through network coupling interfaces, i.e., a handful of scalars. If each component model contains uncertainties represented by a set of parameters, a straightfoward uncertainty quantification (UQ) study would collect all uncertainties into a single set and treat the multiphysics model as a black box. Such an approach ignores the rich structure of the multiphysics system, and the combined space of uncertainties can have a large dimension that prohibits the use of polynomial surrogate models. We propose an intrusive methodology that exploits the structure of the network coupled multiphysics system to efficiently construct a polynomial surrogate of the model output as a function of uncertain inputs. Using a nonlinear elimination strategy, we treat the solution as a composite function: the model outputs are functions of the coupling terms which are functions of the uncertain parameters. The composite structure allows us to construct and employ a reduced polynomial basis that depends on the coupling terms; the basis can be constructed with many fewer system solves than the naive approach, which results in substantial computational savings. We demonstrate the method on an idealized model of a nuclear reactor.