Three dimensional full-field velocity measurements in shock compression experiments using stereo digital image correlation

Ravindran, Suraj; Gandhi, Vatsa; Joshi, A.; Ravichandran, Guruswami

doi:10.48550/arxiv.2210.12568

Cited by 1 publication

(1 citation statement)

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“…These results highlight the potential for ML to transform the field of experimental mechanics, enabling researchers to characterize material properties more efficiently and effectively under extreme conditions. Moving forward, it will be crucial to continue to develop new experimental full-field measurement techniques for experiments under extreme conditions like spatial-temporal interferometer [53] or stereo DIC [284] that can be effectively coupled with ML to generate and analyze large volumes of high-fidelity data.…”

Section: For Experimental Mechanics Under Extreme Conditionsmentioning

confidence: 99%

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

Jin¹,

Zhang²,

Espinosa³

2023

Preprint

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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.

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Section: For Experimental Mechanics Under Extreme Conditionsmentioning

confidence: 99%