让3D网格网络学会感知物体厚度，提升变形预测精度。
Thickness-aware E(3)-Equivariant 3D Mesh Neural Networks
- 引入厚度感知机制，通过数据驱动坐标保持三维平移旋转不变性
- 在工业数据集上实现更精准的节点级三维变形预测
- 适合需要高精度结构分析的工程仿真场景
基于网格的3D静态分析方法近年来作为传统数值求解器的高效替代方案，显著降低了多种物理分析的计算成本与运行时间。然而，这些方法主要关注表面拓扑与几何，常忽略真实3D物体固有的厚度特性，而对立表面间存在高度相关性且行为相似。该局限源于表面间的非连通性及网格内部边连接的缺失。本文提出一种新框架——厚度感知的E(3)等变3D网格神经网络（T-EMNN），在保持表面网格计算效率的同时，有效整合物体厚度信息。此外，我们引入数据驱动坐标，编码空间信息并保持E(3)等变或不变性，确保分析的一致性与鲁棒性。在真实工业数据集上的评估表明，T-EMNN在准确预测节点级3D变形方面表现优异，有效捕捉厚度效应，同时维持计算效率。
原文摘要 · Abstract (English)
Mesh-based 3D static analysis methods have recently emerged as efficient alternatives to traditional computational numerical solvers, significantly reducing computational costs and runtime for various physics-based analyses. However, these methods primarily focus on surface topology and geometry, often overlooking the inherent thickness of real-world 3D objects, which exhibits high correlations and similar behavior between opposing surfaces. This limitation arises from the disconnected nature of these surfaces and the absence of internal edge connections within the mesh. In this work, we propose a novel framework, the Thickness-aware E(3)-Equivariant 3D Mesh Neural Network (T-EMNN), that effectively integrates the thickness of 3D objects while maintaining the computational efficiency of surface meshes. Additionally, we introduce data-driven coordinates that encode spatial information while preserving E(3)-equivariance or invariance properties, ensuring consistent and robust analysis. Evaluations on a real-world industrial dataset demonstrate the superior performance of T-EMNN in accurately predicting node-level 3D deformations, effectively capturing thickness effects while maintaining computational efficiency.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。