MAVEN：一种面向网格的体素编码网络，用于模拟3D柔性变形
MAVEN: A Mesh-Aware Volumetric Encoding Network for Simulating 3D Flexible Deformation
AI总结：
本文提出MAVEN，一种面向网格的体素编码网络，用于模拟3D柔性变形。该网络通过显式建模高维几何网格元素，提高物理模拟的准确性。实验表明，MAVEN在多个数据集和新任务中表现优异。
摘要：
基于深度学习的方法，特别是图神经网络（GNNs），在模拟固体的柔性变形和接触方面取得了显著进展，因为它们能够处理无结构的物理场和在图结构上进行非线性回归。然而，现有的GNNs通常仅使用顶点和边构建网格图。这些方法往往忽略了原始几何中的高维空间特征，例如2D面和3D单元。因此，准确捕捉边界表示和体素特性具有挑战性，尽管这些信息对建模接触交互和内部物理量传播至关重要，尤其是在稀疏网格离散化的情况下。在本文中，我们引入了MAVEN，一种面向网格的体素编码网络，用于模拟3D柔性变形，该网络显式建模高维几何网格元素，以实现更准确和自然的物理模拟。MAVEN建立了3D单元、2D面和顶点之间的可学习映射，使灵活的相互转换成为可能。将显式几何特征纳入模型以减轻隐式学习几何模式的负担。实验结果表明，MAVEN在现有数据集和一个新型金属拉伸弯曲任务中均取得了最先进的性能，该任务具有大变形和长时间接触的特点。
英文摘要：
Deep learning-based approaches, particularly graph neural networks (GNNs), have gained prominence in simulating flexible deformations and contacts of solids, due to their ability to handle unstructured physical fields and nonlinear regression on graph structures. However, existing GNNs commonly represent meshes with graphs built solely from vertices and edges. These approaches tend to overlook higher-dimensional spatial features, e.g., 2D facets and 3D cells, from the original geometry. As a result, it is challenging to accurately capture boundary representations and volumetric characteristics, though this information is critically important for modeling contact interactions and internal physical quantity propagation, particularly under sparse mesh discretization. In this paper, we introduce MAVEN, a mesh-aware volumetric encoding network for simulating 3D flexible deformation, which explicitly models geometric mesh elements of higher dimension to achieve a more accurate and natural physical simulation. MAVEN establishes learnable mappings among 3D cells, 2D facets, and vertices, enabling flexible mutual transformations. Explicit geometric features are incorporated into the model to alleviate the burden of implicitly learning geometric patterns. Experimental results show that MAVEN consistently achieves state-of-the-art performance across established datasets and a novel metal stretch-bending task featuring large deformations and prolonged contacts.
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