Giselle Fernández-Godino, M. Review of Multi-Fidelity Models. Advances in Computational Science and Engineering 2023, 0, 0–0, doi:10.3934/acse.2023015.
Giselle Fernández-Godino, M. Review of Multi-Fidelity Models. Advances in Computational Science and Engineering 2023, 0, 0–0, doi:10.3934/acse.2023015.
Giselle Fernández-Godino, M. Review of Multi-Fidelity Models. Advances in Computational Science and Engineering 2023, 0, 0–0, doi:10.3934/acse.2023015.
Giselle Fernández-Godino, M. Review of Multi-Fidelity Models. Advances in Computational Science and Engineering 2023, 0, 0–0, doi:10.3934/acse.2023015.
Abstract
This article provides an overview of multi-fidelity modeling trends. Fidelity in modeling refers to the level of detail and accuracy provided by a predictive model or simulation. Generally, models with higher fidelity deliver more precise results but demand greater computational resources. Multi-fidelity models integrate high-fidelity and low-fidelity models to obtain fast yet accurate predictions. Their growing popularity is due to their ability to approximate high-fidelity models with high accuracy and low computational cost. This work classifies publications in multi-fidelity modeling based on various factors, including application, surrogate selection, fidelity difference, fidelity combination method, field of application, and year of publication. The study also examines the techniques used to combine fidelities, focusing on multi-fidelity surrogate models. To accurately evaluate the advantages of utilizing multi-fidelity models, it is necessary to report the achieved time savings. This paper includes guidelines for authors to present their multi-fidelity-related savings in a standard, succinct, yet thorough way to guide future users. According to a select group of publications that provided sufficient information, multi-fidelity models achieved savings of up to 90% while maintaining the desired level of accuracy. However, the savings achieved through multi-fidelity models depend highly on the problem.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
