Robust Experimental Designs for Model Calibration

TL;DR

This paper develops a robust optimal experimental design method for calibrating computer models, accounting for potential discrepancies between the model and reality, and demonstrates its advantages over traditional designs.

Contribution

It introduces a novel robust optimal design approach for physical experiments that incorporates potential model discrepancies, improving calibration accuracy.

Findings

Robust designs outperform traditional experimental designs.

The approach effectively accounts for model discrepancy.

Demonstrated success on toy and real industrial examples.

Abstract

A computer model can be used for predicting an output only after specifying the values of some unknown physical constants known as calibration parameters. The unknown calibration parameters can be estimated from real data by conducting physical experiments. This paper presents an approach to optimally design such a physical experiment. The problem of optimally designing physical experiment, using a computer model, is similar to the problem of finding optimal design for fitting nonlinear models. However, the problem is more challenging than the existing work on nonlinear optimal design because of the possibility of model discrepancy, that is, the computer model may not be an accurate representation of the true underlying model. Therefore, we propose an optimal design approach that is robust to potential model discrepancies. We show that our designs are better than the commonly used physical experimental designs that do not make use of the information contained in the computer model and other nonlinear optimal designs that ignore potential model discrepancies. We illustrate our approach using a toy example and a real example from industry.