Perspective: Machine learning in experimental solid mechanics. (April 2023) - Brodnik, N.R.; Muir, C.; Tulshibagwale, N.; Rossin, J.; Echlin, M.P.; Hamel, C.M.; Kramer, S.L.B.; Pollock, T.M.; Kiser, J.D.; Smith, C.; Daly, S.H.
Abstract: Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches are rapidly proliferating into the discovery process due to significant advances in data storage and processing capabilities. Much of the ML that is being adopted by the mechanics community was initially developed for application outside of science and engineering, and has the potential to produce results of questionable physical validity. To ensure that these data-driven approaches are trustworthy, there is a clear need to embed physical principles into their architectures, to evaluate and compare ML frameworks against benchmark datasets, and to test their broader extensibility. Frameworks must be grounded in a clear objective, quantifiable error, and a well-defined scope of extensibility. These principles enable ML models with a wide range of architectures to be meaningfully categorized, compared, evaluated, and extended to broader experimental and computational frameworks. Application of these principles are demonstrated through an investigation of ML models in two different use cases, acoustic emission and resonant ultrasound spectroscopy, along with a discussion of outlooks for the future of trustworthy ML in experimental mechanics. Graphical abstract: Highlights: Core requirements of ML and data approaches in experimental mechanics are discussed Clear Objective: models, their goals, and expectations Quantifiable Evaluation: establish meaningful and statistically accurate error Well-defined Extensibility: generalize models, apply them to broader frameworks Explore role of benchmarking, metrics, and physics in ML models