当前位置: X-MOL 学术J. Mech. Phys. Solids › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Perspective: Machine learning in experimental solid mechanics
Journal of the Mechanics and Physics of Solids ( IF 5.3 ) Pub Date : 2023-01-31 , DOI: 10.1016/j.jmps.2023.105231
N.R. Brodnik, C. Muir, N. Tulshibagwale, J. Rossin, M.P. Echlin, C.M. Hamel, S.L.B. Kramer, T.M. Pollock, J.D. Kiser, C. Smith, S.H. Daly

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.



中文翻译:

观点:实验固体力学中的机器学习

实验固体力学正处于一个关键点,由于数据存储和处理能力的显着进步,机器学习 (ML) 方法正在迅速扩散到发现过程中。力学界正在采用的大部分 ML 最初是为科学和工程以外的应用而开发的,并且有可能产生有问题的物理有效性的结果。为了确保这些数据驱动的方法值得信赖,显然需要将物理原理嵌入到它们的架构中,以评估和比较 ML 框架与基准数据集,并测试它们更广泛的可扩展性。框架必须以明确的目标、可量化的错误和明确定义的可扩展性范围为基础。这些原则使具有广泛体系结构的 ML 模型能够被有意义地分类、比较、评估,并扩展到更广泛的实验和计算框架。这些原则的应用通过在两个不同用例(声发射和共振超声光谱)中对 ML 模型的调查,以及对实验力学中值得信赖的 ML 的未来前景的讨论来证明。

更新日期:2023-01-31
down
wechat
bug