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精确预测。当获取高精度数据成本高昂或计算密集时，这类模型尤其具有优势。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中的应用，特别是在优化与不确定性量化方面。本研究根据应用领域、代理模型选择、保真度类型、组合方法及发表年份等多个标准，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，重点聚焦于多保真度代理模型。本文讨论了可重复性、开源方法及基准测试流程，以促进透明度提升。文中还包含用于教学目的的简化示例问题，以增强理解。此外，本文概述了以标准化、简洁且详尽的方式呈现多保真度相关效益的最佳实践。本综述最后审视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。