Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems

Abstract

Fixed point iteration is a common strategy to handle interdisciplinary coupling within a feedback-coupled multidisciplinary analysis. For each coupled analysis, this requires a large number of disciplinary high-fidelity simulations to resolve the interactions between different disciplines. When embedded within an uncertainty analysis loop (e.g., with Monte Carlo sampling over uncertain parameters), the number of high-fidelity disciplinary simulations quickly becomes prohibitive, because each sample requires a fixed point iteration and the uncertainty analysis typically involves thousands or even millions of samples. This paper develops a method for uncertainty quantification in feedback-coupled systems that leverage adaptive surrogates to reduce the number of cases forwhichfixedpoint iteration is needed. The multifidelity coupled uncertainty propagation method is an iterative process that uses surrogates for approximating the coupling variables and adaptive sampling strategies to refine the surrogates. The adaptive sampling strategies explored in this work are residual error, information gain, and weighted information gain. The surrogate models are adapted in a way that does not compromise the accuracy of the uncertainty analysis relative to the original coupled high-fidelity problem as shown through a rigorous convergence analysis.