arXiv Machine Learning By Songlin Duan, Shuo Yang, Yujia Zheng, Tobin Filleter

Learning Topology-Aware Representations for Mechanics-Relevant TPMS Metamaterials

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arXiv:2610. 20684v1 Announce Type: new Abstract: Learning informative representations of mechanical Triply Periodic Minimal Surface (TPMS) metamaterials is important for mechanics analysis and structural design, yet existing methods have rarely explicitly supervised learned representations with information about global structural connectivity that is relevant to mechanical response.

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arXiv Machine Learning
Jun 18

Unreduced Persistence Diagrams for Topological Machine Learning

arXiv:2507. 07156v2 Announce Type: replace-cross Abstract: Supervised machine learning pipelines trained on features derived from persistent homology have been experimentally observed to ignore much of the information contained in a persistence diagram.

By Nicole Abreu, Parker B. Edwards, Francis Motta