Perspective: Machine learning in experimental solid mechanics

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.

Automated summary

Experimental solid mechanics is experiencing a crucial moment where the integration of machine learning (ML) approaches into the discovery process is rapidly increasing. The adoption of ML methods in mechanics originated from non-science and engineering applications, raising concerns about the reliability of the obtained physical results. To address this, it is necessary to incorporate physical principles into ML architectures, evaluate and compare them using benchmark datasets, and test their broad applicability. These principles allow for meaningful categorization, comparison, evaluation, and extension of ML models across various experimental and computational frameworks. Two different use cases, acoustic emission and resonant ultrasound spectroscopy, are examined to demonstrate the application of these principles and discussions are provided regarding the future prospects of trustworthy ML in experimental mechanics.