Harnessing interpretable machine learning for holistic inverse design of origami

This work harnesses interpretable machine learning methods to address the challenging inverse design problem of origami-inspired systems. We established a work flow based on decision tree-random forest method to fit origami databases, containing both design features and functional performance, and t...

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Detalles Bibliográficos
Autores principales: Zhu, Yi, Filipov, Evgueni T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652322/
https://www.ncbi.nlm.nih.gov/pubmed/36369348
http://dx.doi.org/10.1038/s41598-022-23875-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652322/
https://www.ncbi.nlm.nih.gov/pubmed/36369348
http://dx.doi.org/10.1038/s41598-022-23875-6

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