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Found 3 papers, 3 papers with code
Machine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterials
There has been significant recent interest in the mechanics community to design structures that can either violate reciprocity, or exhibit elastic asymmetry or odd elasticity.
Interpreting and generalizing deep learning in physics-based problems with functional linear models
In this work, motivated by the field of functional data analysis (FDA), we propose generalized functional linear models as an interpretable surrogate for a trained deep learning model.
Towards out of distribution generalization for problems in mechanics
To address this, we investigate the performance of OOD generalization methods for regression problems in mechanics.
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