Efficient sequential experimental design for surrogate modeling of nested codes

TL;DR

This paper introduces an efficient sequential experimental design method for surrogate modeling of nested computer codes, optimizing accuracy and computational cost by exploiting the nested structure and adaptive code selection.

Contribution

It proposes a novel sequential design strategy that leverages the nested structure of codes and a criterion for adaptive code calling to improve surrogate model accuracy.

Findings

Effective in reducing prediction variance

Adaptive code selection improves efficiency

Validated on example problems

Abstract

Thanks to computing power increase, the certification and the conception of complex systems relies more and more on simulation. To this end, predictive codes are needed, which have generally to be evaluated in a huge number of input points. When the computational cost of these codes is high, surrogate models are introduced to emulate the response of these codes. In this paper, we consider the situation when the system response can be modeled by two nested computer codes. By two nested computer codes, we mean that some inputs of the second code are outputs of the first code. More precisely, the idea is to propose sequential designs to improve the accuracy of the nested code's predictor by exploiting the nested structure of the codes. In particular, a selection criterion is proposed to allow the modeler to choose the code to call, depending on the expected learning rate and the computational cost of each code. The sequential designs are based on the minimization of the prediction variance, so adaptations of the Gaussian process formalism are proposed for this particular configuration in order to quickly evaluate the mean and the variance of the predictor. The proposed methods are then applied to examples.