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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Nature Publishing Group UK
2022
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| 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 |