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Hanxun Jin's picture

Journal Club for January 2024: Machine Learning in Experimental Solid Mechanics: Recent Advances, Challenges, and Opportunities

Hanxun Jin (a,b), Horacio D. Espinosa (b)
a Division of Engineering and Applied Science, California Institute of Technology
b Department of Mechanical Engineering, Northwestern University

In recent years, Machine Learning (ML) has become increasingly prominent in Solid Mechanics. Its diverse applications include extracting unknown material parameters, developing surrogate models for constitutive modeling, advancing multiscale modeling, and designing architected materials. In this Journal Club, we will focus our discussion on the recent advances and challenges of ML when experimental data is involved. With broad community interest, as reflected by the increasing number of publications in this field, we have recently published a review article in Applied Mechanics Reviews titled “Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review”. Moreover, a recent insightful paper from Prof. Sam Daly’s group also discussed some perspectives in this field. In this Journal Club, we would like to introduce and share insights into this exciting field.

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We're excited to announce an opening for a Postdoctoral Researcher with expertise in Mechanics or Optics. Whether your strength lies in experimental techniques or computational methods, we welcome your application.

Start Date: Immediate

Keywords: Mechanics, Optics, Machine Learning

More info: athanasiou@gatech.edu

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tarkes's picture

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Dear Colleagues,

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PeterMcHugh's picture

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Fabrice Pierron's picture

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