Multifidelity Surrogate Models: A New Data Fusion Perspective

TL;DR

This paper introduces a novel multifidelity surrogate modeling approach that constructs gradient-only surrogates using only gradient information, enhancing data fusion efficiency and accuracy.

Contribution

It proposes a new data fusion method based on gradient-only surrogates, addressing challenges in fidelity level selection and improving surrogate model construction.

Findings

Effective on foundational example problems

Demonstrates improved data fusion accuracy

Simplifies surrogate construction process

Abstract

Multifidelity surrogate modelling combines data of varying accuracy and cost from different sources. It strategically uses low-fidelity models for rapid evaluations, saving computational resources, and high-fidelity models for detailed refinement. It improves decision-making by addressing uncertainties and surpassing the limits of single-fidelity models, which either oversimplify or are computationally intensive. Blending high-fidelity data for detailed responses with frequent low-fidelity data for quick approximations facilitates design optimisation in various domains. Despite progress in interpolation, regression, enhanced sampling, error estimation, variable fidelity, and data fusion techniques, challenges persist in selecting fidelity levels and developing efficient data fusion methods. This study proposes a new fusion approach to construct multi-fidelity surrogate models by constructing gradient-only surrogates that use only gradients to construct regression surfaces. Results are demonstrated on foundational example problems that isolate and illustrate the fusion approach's efficacy, avoiding the need for complex examples that obfuscate the main concept.