Review of multi-fidelity models

TL;DR

This comprehensive review analyzes multi-fidelity models, highlighting their applications, techniques for combining different fidelity levels, and best practices for transparency and benchmarking in scientific and engineering contexts.

Contribution

It provides a detailed classification of multi-fidelity modeling approaches, discusses reproducibility and benchmarking, and outlines current trends and future research directions.

Findings

Emphasizes the importance of surrogate models in multi-fidelity frameworks.

Highlights the need for standardized benchmarking and transparency.

Identifies emerging techniques and promising research areas.

Abstract

Multi-fidelity models provide a framework for integrating computational models of varying complexity, allowing for accurate predictions while optimizing computational resources. These models are especially beneficial when acquiring high-accuracy data is costly or computationally intensive. This review offers a comprehensive analysis of multi-fidelity models, focusing on their applications in scientific and engineering fields, particularly in optimization and uncertainty quantification. It classifies publications on multi-fidelity modeling according to several criteria, including application area, surrogate model selection, types of fidelity, combination methods and year of publication. The study investigates techniques for combining different fidelity levels, with an emphasis on multi-fidelity surrogate models. This work discusses reproducibility, open-sourcing methodologies and benchmarking procedures to promote transparency. The manuscript also includes educational toy problems to enhance understanding. Additionally, this paper outlines best practices for presenting multi-fidelity-related savings in a standardized, succinct and yet thorough manner. The review concludes by examining current trends in multi-fidelity modeling, including emerging techniques, recent advancements, and promising research directions.