A Parallel Direct Finite Element Solver Empowered by Machine Learning for Solving Large-Scale Electromagnetic Problems

In this communication, a parallel direct finite element solver is proposed for solving complex electromagnetic problems. Our proposed solver features adaptive fill-reducing ordering and highly assembled lazy offloading (HALO) factorization. In our adaptive fill-reducing ordering approach, we propose...

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Veröffentlicht in:	IEEE transactions on antennas and propagation Jg. 73; H. 4; S. 2690 - 2695
Hauptverfasser:	Wang, Junjie, Zuo, Sheng, Zhao, Xunwang, Tian, Min, Lin, Zhongchao, Zhang, Yu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accelerators Aggregates Algorithms Antennas and propagation Artificial neural networks Complex structures Data transfer (computers) Electromagnetics Factorization Feature extraction Finite element analysis finite element method (FEM) Format graph convolutional network (GCN) Graph convolutional networks large-scale parallel algorithm Machine learning manycore architecture Parallel processing radiation problems Solvers Sparse matrices Sunway computing systems supernodal factorization algorithm Training Vectors
ISSN:	0018-926X, 1558-2221
Online-Zugang:	Volltext
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