Sensitivity-driven adaptive construction of reduced-space surrogates

TL;DR

This paper presents a systematic, sensitivity-driven framework for constructing reduced-space surrogate models, significantly improving computational efficiency in complex uncertain systems.

Contribution

It introduces an iterative screening method using derivative-based sensitivity measures to identify unimportant inputs for surrogate construction.

Findings

Efficient surrogate models reduce computational costs.

Framework successfully applied to chemical kinetics problem.

Significant accuracy gains with reduced input dimensions.

Abstract

We develop a systematic approach for surrogate model construction in reduced input parameter spaces. A sparse set of model evaluations in the original input space is used to approximate derivative based global sensitivity measures (DGSMs) for individual uncertain inputs of the model. An iterative screening procedure is developed that exploits DGSM estimates in order to identify the unimportant inputs. The screening procedure forms an integral part of an overall framework for adaptive construction of a surrogate in the reduced space. The framework is tested for computational efficiency through an initial implementation in simple test cases such as the classic Borehole function, and a semilinear elliptic PDE with a random source term. The framework is then deployed for a realistic application from chemical kinetics, where we study the ignition delay in an H2/O2 reaction mechanism with 19 uncertain rate constants. It is observed that significant computational gains can be attained by constructing accurate low-dimensional surrogates using the proposed framework.