An efficient methodology for the analysis and modeling of computer experiments with large number of inputs

TL;DR

This paper introduces a general, efficient methodology for building Gaussian process metamodels with many inputs, enabling accurate predictions from minimal experiments, especially useful for complex, high-dimensional computer codes.

Contribution

The paper presents a novel, scalable approach to constructing Gaussian process metamodels for high-dimensional problems, overcoming computational challenges and enabling effective variable selection.

Findings

Successfully applied to an industrial computer code

Achieves high predictive accuracy with minimal experiments

Applicable to various types of metamodels

Abstract

Complex computer codes are often too time expensive to be directly used to perform uncertainty, sensitivity, optimization and robustness analyses. A widely accepted method to circumvent this problem consists in replacing cpu-time expensive computer models by cpu inexpensive mathematical functions, called metamodels. For example, the Gaussian process (Gp) model has shown strong capabilities to solve practical problems , often involving several interlinked issues. However, in case of high dimensional experiments (with typically several tens of inputs), the Gp metamodel building process remains difficult, even unfeasible, and application of variable selection techniques cannot be avoided. In this paper, we present a general methodology allowing to build a Gp metamodel with large number of inputs in a very efficient manner. While our work focused on the Gp metamodel, its principles are fully generic and can be applied to any types of metamodel. The objective is twofold: estimating from a minimal number of computer experiments a highly predictive metamodel. This methodology is successfully applied on an industrial computer code.