Harnessing interpretable machine learning for holistic inverse design of origami.

ABSTRACT: 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 to generate human-understandable decision rules for the inverse design of functional origami. First, the tree method is unique because it can handle complex interactions between categorical features and continuous features, allowing it to compare different origami patterns for a design. Second, this interpretable method can tackle multi-objective problems for designing functional origami with multiple and multi-physical performance targets. Finally, the method can extend existing shape-fitting al

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC9652322 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature