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

TL;DR

This review summarizes recent machine learning advancements applied to experimental solid mechanics, highlighting algorithms, applications, challenges, and future directions to aid researchers in integrating ML into their experimental workflows.

Contribution

It provides a comprehensive overview of recent ML methods and applications in experimental solid mechanics, emphasizing physics-informed approaches and addressing current challenges.

Findings

ML enhances experimental design and data analysis in solid mechanics

Physics-informed ML methods are increasingly prominent

Identifies challenges in multi-fidelity data integration

Abstract

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.