Uncertainty-aware multi-fidelity surrogate modeling with noisy data

TL;DR

This paper presents a new multi-fidelity surrogate modeling framework that effectively manages noisy data, providing accurate uncertainty estimates and integrating experimental and computational models for high-fidelity predictions.

Contribution

It introduces a comprehensive approach to handle noise in multi-fidelity surrogate models, enabling precise uncertainty quantification and combining physical experiments with computational models.

Findings

Effective noise handling in surrogate models

Accurate uncertainty quantification with confidence intervals

Successful application to wind turbine data

Abstract

Emulating high-accuracy computationally expensive models is crucial for tasks requiring numerous model evaluations, such as uncertainty quantification and optimization. When lower-fidelity models are available, they can be used to improve the predictions of high-fidelity models. Multi-fidelity surrogate models combine information from sources of varying fidelities to construct an efficient surrogate model. However, in real-world applications, uncertainty is present in both high- and low-fidelity models due to measurement or numerical noise, as well as lack of knowledge due to the limited experimental design budget. This paper introduces a comprehensive framework for multi-fidelity surrogate modeling that handles noise-contaminated data and is able to estimate the underlying noise-free high-fidelity model. Our methodology quantitatively incorporates the different types of uncertainty affecting the problem and emphasizes on delivering precise estimates of the uncertainty in its predictions both with respect to the underlying high-fidelity model and unseen noise-contaminated high-fidelity observations, presented through confidence and prediction intervals, respectively. Additionally, the proposed framework offers a natural approach to combining physical experiments and computational models by treating noisy experimental data as high-fidelity sources and white-box computational models as their low-fidelity counterparts. The effectiveness of our methodology is showcased through synthetic examples and a wind turbine application.