发表机构
University of Thessaly（色萨利大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
DeepFEAv2提出一种深度学习代理框架，利用FE连通性矩阵组织输入并设计新架构，实现跨网格拓扑和单元类型的瞬态FEA预测，R²高达0.99，误差低至0.38%，推理速度提升三个数量级。
AI 中文摘要
有限元分析（FEA）被广泛用于瞬态力学仿真，但其高昂的计算成本限制了实时和高分辨率应用。深度学习代理模型可以降低这一成本；然而，许多现有方法局限于稳态预测，或者无法随时间联合预测基于节点和基于单元的输出（NEO）。最先进的DeepFEA框架解决了这些问题，但仍局限于结构化有限元（FE）网格。为克服这一局限，本研究提出DeepFEAv2，一种深度学习代理框架，能够跨不同FE网格拓扑和单元类型预测瞬态FEA仿真。DeepFEAv2的主要贡献包括：（a）一个利用FE连通性矩阵按单元组织输入特征，并根据网格拓扑将其排列成输入序列的模块；（b）一种新颖的神经网络架构，用于处理输入序列并随时间联合预测NEO；（c）一种基于FEA的优化策略，用于正则化这些NEO预测。DeepFEAv2在结构化和非结构化3D线弹性数据集以及压力驱动的主动脉瓣数据集上进行了评估。DeepFEAv2实现了高达0.99的R²值和低至0.38%的归一化误差。与DeepFEA相比，其R²相对提升高达38.0%，归一化误差降低高达87.1%。DeepFEAv2的推理速度也比传统FEA快多达三个数量级。这些结果表明，DeepFEAv2能够在日益复杂的FE设置中高效建模瞬态FEA仿真，为瞬态FEA提供可扩展的代理框架。
英文摘要
Finite Element Analysis (FEA) is widely used for transient mechanical simulations, but its high computational cost limits real-time and high-resolution applications. Deep learning surrogate models can reduce this cost; however, many existing approaches are restricted to steady-state prediction or cannot jointly predict Node- and Element-based Outputs (NEO) over time. The state-of-the-art DeepFEA framework has addressed these issues but remains limited to structured finite element (FE) meshes. To overcome this limitation, this study proposes DeepFEAv2, a deep learning surrogate framework that enables prediction of transient FEA simulations across different FE mesh topologies and element types. The main contributions of DeepFEAv2 are: (a) a module that uses the FE connectivity matrix to organize input features by element and arrange them into an input sequence guided by the mesh topology; (b) a novel neural network architecture designed to process the input sequence and jointly predict NEO over time; and (c) a FEA-informed optimization strategy for regularizing these NEO predictions. DeepFEAv2 was evaluated on structured and unstructured 3D linear elastic datasets, as well as on a pressure-driven aortic valve dataset. DeepFEAv2 achieved R^2 values up to 0.99 and normalized errors as low as 0.38%. Compared with DeepFEA, it achieved up to 38.0% relative increase in R^2 and up to 87.1% reduction in normalized error. DeepFEAv2 also performed inference up to three orders of magnitude faster than traditional FEA. These results demonstrate that DeepFEAv2 can efficiently model transient FEA simulations across increasingly complex FE settings, providing a scalable surrogate framework for transient FEA.
