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Found 4 papers, 1 papers with code
Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators
Our work marks a significant advancement in the field of materials-by-design, potentially heralding a new era in the discovery and development of next-generation metamaterials with unparalleled mechanical characteristics derived directly from experimental insights.
Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks
In this paper, we introduce a robust PINN-based framework designed to identify material parameters for soft materials, specifically those exhibiting complex constitutive behaviors, under large deformation in plane stress conditions.
Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
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.
Dynamic fracture of a bicontinuously nanostructured copolymer: A deep-learning analysis of big-data-generating experiment
For the first time, the DCPs of polyurea have been successfully obtained by the DL-ISI with the pre-trained CNN and correlation analyses of cGAN-reconstructed data sets.
