Shape-morphing programming of soft materials on complex geometries via neural operator
Lu Chen, Gengxiang Chen, Xu Liu, Jingyan Su, Xuhao Lyu, Lihui Wang, Yingguang Li

TL;DR
This paper introduces S2NO, a neural operator that predicts and optimizes shape-morphing soft materials on complex geometries, enabling high-fidelity, diverse, and super-resolution morphing designs for advanced applications.
Contribution
The paper presents a novel neural operator combining spectral and spatial methods for accurate morphing prediction and optimization on complex geometries, including super-resolution capabilities.
Findings
S2NO accurately predicts shape morphing on irregular geometries.
Combining S2NO with evolutionary algorithms enables voxel-level optimization.
The method achieves super-resolution design for complex morphing applications.
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…
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Taxonomy
TopicsAdvanced Materials and Mechanics · 3D Shape Modeling and Analysis · Topology Optimization in Engineering
