Reliable surrogate modeling of engineering data with more than two levels of fidelity

Abstract

Surrogate modeling problems often include variable fidelity data. Most approaches consider the case of two available levels of fidelity, while engineers can have data with more than two samples sorted by fidelity. We consider Gaussian process regression framework that can construct surrogate models with arbitrary number of fidelity levels. While straightforward implementation struggles from numerical instability and numerical problems, our approach adopts Bayesian paradigm and provides direct control of numerical properties of surrogate model construction problems. Benchmark of the presented approach consists of various artificial and real data problems with the focus on surrogate modeling of an airfoil and a C-shape press.