Physics-Informed Neural Networks for Programmable Origami Metamaterials with Controlled Deployment
Sukheon Kang, Youngkwon Kim, Jinkyu Yang, Seunghwa Ryu · arXiv · 2025
A data-free physics-informed neural network enables inverse design of conical Kresling origami metamaterials with programmable multistable energy barriers and controlled, sequential deployment validated on prototypes.
Plain English summary
Why this matters
Key findings
- PINN framework enables forward prediction of complete energy landscapes for conical Kresling origami without pre-collected training data.
- Inverse design can set target stable-state heights and separating energy barriers to program the full energy curve.
- Hierarchical conical Kresling assemblies can achieve sequential layer-by-layer deployment by programming barrier magnitudes.
- Finite element simulations and experiments on physical prototypes validate designed deployment sequences and barrier ratios.
- The method reduces non-physical artifacts by embedding mechanical equilibrium into learning.
Limitations
The abstract does not specify the range of geometries/materials, computational cost, sensitivity to modeling assumptions, or how general the method is beyond the conical Kresling origami cases studied.
Publication
- Publisher
- arXiv
- Publication date
- August 19, 2025
- Research type
- Preprint
- arXiv
- 2508.13559
- Access
- open
Tags
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