For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantification, and inverse problems. As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics. Here, we first provide an overview of common ML algorithms and terminologies that are pertinent to this review, with emphasis placed on physics-informed and physics-based ML methods. Then, we provide thorough coverage of recent ML applications in traditional and emerging areas of experimental mechanics, including fracture mechanics, biomechanics, nano- and micro-mechanics, architected materials, and 2D material. Finally, we highlight some current challenges of applying ML to multi-modality and multi-fidelity experimental datasets and propose several future research directions. This review aims to provide valuable insights into the use of ML methods as well as a variety of examples for researchers in solid mechanics to integrate into their experiments.

翻译：数十年以来，实验固体力学在表征和理解天然及新型材料的力学性能方面一直发挥着关键作用。机器学习的最新进展为该领域带来了新机遇，包括实验设计、数据分析、不确定性量化和反问题求解。鉴于近年来该新兴领域发表的论文数量激增，对机器学习在实验固体力学中的最新应用进行全面且与时俱进的综述正当其时。本文首先概述了与本综述相关的常见机器学习算法及术语，重点关注物理信息驱动和基于物理的机器学习方法。随后，我们全面涵盖了机器学习在断裂力学、生物力学、纳观与微观力学、拓扑优化材料及二维材料等传统与新兴实验力学领域的最新应用。最后，我们指出了将机器学习应用于多模态与多保真度实验数据集当前面临的若干挑战，并提出了若干未来研究方向。本综述旨在为固体力学领域的研究人员将机器学习方法整合至其实验中提供宝贵的见解与多样化的实例。