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arXiv 2601.09595math.NAcs.NA
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神经近似虚拟元方法的两个连续扩展

Two continuous extensions of the Neural Approximated Virtual Element Method

Politecnico di Torino(都灵理工大学);Sorbonne Université(索邦大学);Inria(法国国家数字科学研究所)

Stefano Berrone, Moreno Pintore, Gioana Teora

AI总结:

本文提出两种基于神经网络的连续扩展方法,用于提升虚拟元方法在连续性和精度上的性能。

摘要:

我们提出了两种全局连续的神经基变体,即B-NAVEM和P-NAVEM,作为神经近似虚拟元方法(NAVEM)的连续扩展。两种方法均利用预训练的全连接神经网络构建局部基函数,同时确保相邻网格元素间的精确连续性。B-NAVEM利用物理信息神经网络近似求解定义虚拟元基函数的局部拉普拉斯问题,而P-NAVEM则通过定制的损失函数直接强制多项式可重现性,无需在元素内部要求调和性。数值实验在训练和测试阶段评估了这些方法的计算成本、内存使用和准确性。

英文摘要:

We propose two globally continuous neural-based variants of the Neural Approximated Virtual Element Method (NAVEM), termed B-NAVEM and P-NAVEM. Both approaches construct local basis functions using pre-trained fully connected neural networks while ensuring exact continuity across adjacent mesh elements. B-NAVEM leverages a Physics-Informed Neural Network to approximately solve the local Laplace problem that defines the virtual element basis functions, whereas P-NAVEM directly enforces polynomial reproducibility via a tailored loss function, without requiring harmonicity within the element interior. Numerical experiments assess the methods in terms of computational cost, memory usage, and accuracy during both training and testing phases.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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