用神经算子实现复杂形状软材料的精准变形编程
Shape-morphing programming of soft materials on complex geometries via neural operator
- 提出S2NO神经算子，融合谱与空间卷积捕捉复杂几何上的全局局部变形
- 在不规则边界、多孔、薄壁结构上实现高保真变形预测与优化设计
- 具备网格无关性，支持超分辨率材料分布设计，适合先进软体机器人应用
可变形软材料可通过体素级材料分布设计实现多种目标形态，具有广泛应用潜力。尽管在简单几何上已有进展，但实现如贴合植入或气动变形等高级应用仍需在复杂几何上实现精确且多样化的变形设计，这仍是挑战。本文提出谱-空间神经算子（S2NO），可在复杂几何上实现高保真变形预测。S2NO通过结合拉普拉斯特征函数编码与空间卷积，有效捕捉不规则计算域上的全局与局部变形行为。将S2NO与进化算法结合，实现了在不规则边界形状、多孔结构和薄壁结构等多种复杂几何上的体素级材料分布优化，用于形状变形编程。此外，神经算子的网格无关性支持超分辨率材料分布设计，进一步拓展了变形设计的多样性与复杂度。这些进展显著提升了复杂形状变形编程的效率与能力。
原文摘要 · Abstract (English)
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.
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