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

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.

Algorithmic material selection for wearable medical devices a genetic algorithm-based framework with multiscale modeling

“…The research methodology employed in this study for developing and evaluating the algorithmic material selection framework for wearable medical devices is characterized by a multifaceted approach, combining computational modeling, data visualization, and performance assessment. The study begins by defining a set of diverse materials (Material A, Material B, Material C, and Material D) and assigning corresponding performance scores to establish a baseline for the algorithm's evaluation (Jin H. et al, 2023). Subsequently, a bar chart is generated to visually represent the performance scores of these materials, providing an initial overview of their comparative performance in the context of wearable medical devices.…”