Machine Learning-Guided Design of Non-Reciprocal and Asymmetric Elastic Chiral Metamaterials

arXiv:2404.13215v1 Announce Type: cross
Abstract: 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. While these properties are highly desirable to enable mechanical metamaterials to exhibit novel wave propagation phenomena, it remains an open question as to how to design passive structures that exhibit both significant non-reciprocity and elastic asymmetry. In this paper, we first define several design spaces for chiral metamaterials leveraging specific …

abstract arxiv community cs.lg design elastic elasticity machine machine learning novel physics.app-ph propagation type