Physics-Informed Neural Networks for Programmable Origami Metamaterials with Controlled Deployment

TL;DR

This paper introduces a physics-informed neural network framework for designing and predicting the mechanical behavior of origami-inspired structures, enabling precise control over deployment and energy landscapes without pre-existing training data.

Contribution

The authors develop a data-free PINN approach that embeds mechanical equations for inverse design of origami metamaterials, including hierarchical assemblies, validated by simulations and experiments.

Findings

High-accuracy energy landscape predictions

Inverse design of target stable states and barriers

Validated deployment sequences in physical prototypes

Abstract

Origami-inspired structures provide unprecedented opportunities for creating lightweight, deployable systems with programmable mechanical responses. However, their design remains challenging due to complex nonlinear mechanics, multistability, and the need for precise control of deployment forces. Here, we present a physics-informed neural network (PINN) framework for both forward prediction and inverse design of conical Kresling origami (CKO) without requiring pre-collected training data. By embedding mechanical equilibrium equations directly into the learning process, the model predicts complete energy landscapes with high accuracy while minimizing non-physical artifacts. The inverse design routine specifies both target stable-state heights and separating energy barriers, enabling freeform programming of the entire energy curve. This capability is extended to hierarchical CKO assemblies, where sequential layer-by-layer deployment is achieved through programmed barrier magnitudes. Finite element simulations and experiments on physical prototypes validate the designed deployment sequences and barrier ratios, confirming the robustness of the approach. This work establishes a versatile, data-free route for programming complex mechanical energy landscapes in origami-inspired metamaterials, offering broad potential for deployable aerospace systems, morphing structures, and soft robotic actuators.