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For many decades, experimental solid mechanics has played a crucial role incharacterizing and understanding the mechanical properties of natural and novelmaterials. Recent advances in machine learning (ML) provide new opportunitiesfor the field, including experimental design, data analysis, uncertaintyquantification, and inverse problems. As the number of papers published inrecent years in this emerging field is exploding, it is timely to conduct acomprehensive and up-to-date review of recent ML applications in experimentalsolid mechanics. Here, we first provide an overview of common ML algorithms andterminologies that are pertinent to this review, with emphasis placed onphysics-informed and physics-based ML methods. Then, we provide thoroughcoverage of recent ML applications in traditional and emerging areas ofexperimental mechanics, including fracture mechanics, biomechanics, nano- andmicro-mechanics, architected materials, and 2D material. Finally, we highlightsome current challenges of applying ML to multi-modality and multi-fidelityexperimental datasets and propose several future research directions. Thisreview aims to provide valuable insights into the use of ML methods as well asa variety of examples for researchers in solid mechanics to integrate intotheir experiments.

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review