[2604.04474] MAVEN: A Mesh-Aware Volumetric Encoding Network for Simulating 3D Flexible Deformation

Computer Science > Machine Learning arXiv:2604.04474 (cs) [Submitted on 6 Apr 2026] Title:MAVEN: A Mesh-Aware Volumetric Encoding Network for Simulating 3D Flexible Deformation Authors:Zhe Feng, Shilong Tao, Haonan Sun, Shaohan Chen, Zhanxing Zhu, Yunhuai Liu View a PDF of the paper titled MAVEN: A Mesh-Aware Volumetric Encoding Network for Simulating 3D Flexible Deformation, by Zhe Feng and 5 other authors View PDF HTML (experimental) Abstract: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 m...