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.

翻译：多保真度模型提供了一个整合不同复杂度计算模型的框架，能够在优化计算资源的同时实现准确预测。这些模型在获取高精度数据成本高昂或计算密集时尤为有益。本综述对多保真度模型进行了全面分析，重点关注其在科学与工程领域（特别是优化与不确定性量化）的应用。我们根据应用领域、代理模型选择、保真度类型、组合方法及发表年份等多项标准，对多保真度建模相关文献进行了分类。研究探讨了不同保真度层级的组合技术，尤其聚焦于多保真度代理模型。本文讨论了可重复性、开源方法论及基准测试流程以促进透明度，并包含教育性玩具问题以增强理解。此外，本文概述了以标准化、简洁且全面方式呈现多保真度相关效益的最佳实践。本综述最后考察了多保真度建模的当前趋势，包括新兴技术、最新进展及有前景的研究方向。