Shape-morphing programming of soft materials on complex geometries via neural operator

2026-01-16

AI Summary

Designing high-fidelity, diverse deformations in soft materials over complex geometries remains a significant challenge, hindering their application in advanced domains such as conformal implants and pneumatic actuators. This work proposes the Spectral-Space Neural Operator (S2NO), which introduces neural operators to deformation programming of soft materials on intricate geometries for the first time. By leveraging Laplacian eigenfunction encoding and spatial convolutions, S2NO efficiently models both global and local deformation behaviors on irregular domains. Integrated with an evolutionary algorithm, it enables voxel-level material distribution optimization. The method exhibits discretization invariance and super-resolution capabilities, substantially improving the accuracy and efficiency of high-fidelity deformation prediction and programming for complex architectures—including porous and thin-walled structures—thereby greatly expanding the achievable diversity and complexity of programmable deformations.

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Abstract

Shape-morphing soft materials can enable diverse target morphologies through voxel-level material distribution design, offering significant potential for various applications. Despite progress in basic shape-morphing design with simple geometries, achieving advanced applications such as conformal implant deployment or aerodynamic morphing requires accurate and diverse morphing designs on complex geometries, which remains challenging. Here, we present a Spectral and Spatial Neural Operator (S2NO), which enables high-fidelity morphing prediction on complex geometries. S2NO effectively captures global and local morphing behaviours on irregular computational domains by integrating Laplacian eigenfunction encoding and spatial convolutions. Combining S2NO with evolutionary algorithms enables voxel-level optimisation of material distributions for shape morphing programming on various complex geometries, including irregular-boundary shapes, porous structures, and thin-walled structures. Furthermore, the neural operator's discretisation-invariant property enables super-resolution material distribution design, further expanding the diversity and complexity of morphing design. These advancements significantly improve the efficiency and capability of programming complex shape morphing.

Problem

• shape-morphing
• complex geometries
• soft materials
• material distribution
• voxel-level design

Innovation

• neural operator
• shape-morphing
• complex geometries
• voxel-level optimization
• discretization-invariant