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. Keywords: Multi-fidelity, Variable-complexity, Variable-fidelity, Surrogate models, Optimization, Uncertainty quantification, Review, Survey

翻译：本文概述了多保真度建模的发展趋势。建模中的保真度指预测模型或仿真所提供的细节水平与准确性。通常，高保真度模型能提供更精确的结果，但需要更多的计算资源。多保真度模型整合了高保真度与低保真度模型，以实现快速而准确的预测。它们日益普及的原因在于能够以较高精度和较低计算成本近似高保真度模型。本研究基于多种因素对多保真度建模的相关文献进行分类，包括应用领域、代理模型选择、保真度差异、保真度组合方法、应用领域及发表年份。研究还考察了用于组合保真度的技术，重点关注多保真度代理模型。为准确评估使用多保真度模型的优势，需报告所实现的时间节省。本文为作者提供了指南，以便以标准、简洁且全面的方式呈现其多保真度相关节省，从而指导未来用户。根据一组提供了充分信息的精选文献，多保真度模型在保持所需准确度的同时，实现了高达90%的节省。然而，多保真度模型所实现的节省高度依赖于具体问题。关键词：多保真度、可变复杂度、可变保真度、代理模型、优化、不确定性量化、综述、调研