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Updated: Jun 19, 2026

Origami Inspired Self-assembly of Patterned and Reconfigurable Particles
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Physics-informed neural networks for programmable origami metamaterials with controlled deployment.

Sukheon Kang1, Youngkwon Kim2, Jinkyu Yang2

  • 1Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. ryush@kaist.ac.kr.

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Summary

This study introduces a data-free physics-informed neural network (PINN) for designing origami structures. The framework enables precise control over mechanical energy landscapes in deployable metamaterials.

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computational Science

Background:

  • Origami-inspired structures offer lightweight, deployable systems with programmable mechanical properties.
  • Designing these structures is complex due to nonlinear mechanics, multistability, and precise deployment force control requirements.

Purpose of the Study:

  • To develop a physics-informed neural network (PINN) framework for the forward prediction and inverse design of conical Kresling origami (CKO).
  • To enable data-free design of complex mechanical energy landscapes in origami-inspired metamaterials.

Main Methods:

  • A PINN framework was developed, embedding mechanical equilibrium equations directly into the learning process.
  • The model performs forward prediction of energy landscapes and inverse design for target stable-state heights and energy barriers.
  • The approach was extended to hierarchical CKO assemblies for sequential layer-by-layer deployment.

Main Results:

  • The PINN framework accurately predicts complete energy landscapes with minimal non-physical artifacts.
  • The inverse design routine allows for freeform programming of energy curves, including stable states and energy barriers.
  • Validation through finite element simulations and physical prototypes confirmed designed deployment sequences and barrier ratios.

Conclusions:

  • This work presents a versatile, data-free method for programming mechanical energy landscapes in origami-inspired metamaterials.
  • The approach facilitates the design of deployable aerospace systems, morphing structures, and soft robotic actuators.