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.
