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arXiv 2409.15917math.NAcs.NA
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多边形单元上的最低阶神经近似虚拟元方法

The lowest-order Neural Approximated Virtual Element Method on polygonal elements

Stefano Berrone, Moreno Pintore, Gioana Teora

AI总结:

本文提出最低阶神经近似虚拟元方法,用神经网络局部近似虚拟元基函数以消除稳定化和投影算子问题,并通过数值实验验证其在一般多边形网格及带悬挂节点三角形网格上的优势。

摘要:

本文提出了多边形单元上的最低阶神经近似虚拟元方法。该方法采用神经网络对虚拟元基函数进行局部近似,从而消除了标准虚拟元方法中关于稳定化和投影算子的问题,而这两者正是标准虚拟元方法的关键组成部分。我们针对神经网络训练提出了不同的训练策略,每种策略都有相应的理论依据,并具有不同的精度水平。我们进行了若干数值实验,以在一般多边形网格上验证所提方法的有效性,并展示了该方法在不同问题形式下的优势,特别是在大量使用投影项和稳定化项可能对标准版本方法构成挑战的情形中。特别关注了带悬挂节点的三角形网格,这类网格在许多虚拟元应用中具有核心地位。

英文摘要:

The lowest-order Neural Approximated Virtual Element Method on polygonal elements is proposed here. This method employs a neural network to locally approximate the Virtual Element basis functions, thereby eliminating issues concerning stabilization and projection operators, which are the key components of the standard Virtual Element Method. We propose different training strategies for the neural network training, each correlated by the theoretical justification and with a different level of accuracy. Several numerical experiments are proposed to validate our procedure on general polygonal meshes and demonstrate the advantages of the proposed method across different problem formulations, particularly in cases where the heavy usage of projection and stabilization terms may represent challenges for the standard version of the method. Particular attention is reserved to triangular meshes with hanging nodes which assume a central role in many virtual element applications.

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