Review of multi-fidelity models

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 revi...

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Bibliographic Details
Published in:Advances in Computational Science and Engineering Vol. 1; no. 4; pp. 351 - 400
Main Author: Giselle Fernández-Godino, M.
Format: Journal Article
Language:English
Published: United States American Institute of Mathematical Sciences 01.12.2023
Subjects:
ENGINEERING
MATHEMATICS AND COMPUTING
multi-fidelity
optimization
review
surrogate models
survey
uncertainty quantification
variable-complexity
variable-fidelity
uncertainty quantification
Multi-fidelity
optimization
review
surrogate models
variable-fidelity
survey
variable-complexity
ISSN:2837-1739, 2837-1739
Online Access:Get full text
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Summary: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.
Bibliography:AC52-07NA27344
LLNL-JRNL-847790
USDOE Laboratory Directed Research and Development (LDRD) Program
USDOE National Nuclear Security Administration (NNSA)
ISSN:2837-1739
2837-1739
DOI:10.3934/acse.2023015