具有不可见结构的量子图的一种可转移的基于pup的方法

Q3 Engineering

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI:10.1016/j.ifacol.2025.03.013

Csongor L. Laczkó , Mihály A. Vághy , Mihály Kovács

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引用次数: 0

摘要

本研究介绍了一种利用物理信息神经网络（pinn）在度量图（通常称为量子图）上求解偏微分方程（PDEs）的可转移方法。与受特定图结构约束的传统求解器不同，我们的方法利用Neumann-Neumann域分解技术，提供跨各种网络拓扑结构的适应性。通过合并边缘代理，该方法可以获得与FEM在不同网络配置中获得的实验结果相当的实验结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A transferable PINN-based method for quantum graphs with unseen structure⁎

This study introduces a transferable approach for solving partial differential equations (PDEs) on metric graphs, often called quantum graphs, employing Physics-Informed Neural Networks (PINNs). Unlike traditional solvers constrained by specific graph structures, our method utilizes a Neumann-Neumann domain decomposition technique, offering adaptability across various network topologies. By incorporating edge-wise surrogates, this approach achieves experimental results comparable to those obtained with FEM across diverse network configurations.

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来源期刊

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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