Extreme Learning Machineを用いた磁界系Physics-informedニューラルネットワークの検討

カテゴリ: 研究会(論文単位)

論文No: SA23023,RM23023

グループ名: 【B】電力・エネルギー部門 静止器/【D】産業応用部門 回転機合同研究会

発行日: 2023/02/27

タイトル(英語): An investigation of Physics-informed Neural Network for Magnetostatic Problems Based on Extreme Learning Machine

著者名: 佐藤 孝洋(室蘭工業大学),佐々木 秀徳(法政大学),佐藤 佑樹(青山学院大学)

著者名(英語): Takahiro Sato(Muroran Institute of Technology),Hidenori Sasaki(Hosei University),Yuki Sato(Aoyama Gakuin University)

キーワード: ニューラルネットワーク|磁界解析|有限要素法|Neural network|Magnetic analysis| Finite element method

要約(日本語): 微分方程式をニューラルネットワークで表現するPhysics-informedニューラルネットワークに注目が集まっているが、計算量が多い課題がある。本研究ではExtreme Learning Machineを用いた高速なネットワーク学習法について検討する。

要約(英語): The deep learning has been rapidly progressed. Especially, some deep neural networks for solving partial differential equations are aggressively developed. Such deep learning techniques are called the physics-informed neural networks (PINNs). Although PINNs have great potentials to drastically change the current numerical analysis processes, their current drawbacks are heavy computational cost due to the backpropagation learning. On the other hand, there is a neural network approach called the extreme learning machine (ELM), whose computational cost is relatively light because it does not use the backpropagation-based learning. Thus, it is an interesting approach to combine ELM and PINNs for the fast construction of the neural networks for solving partial differential equations. In this paper, a new physics-informed extreme learning machine (PIELM) for solving magnetostatic problems is presented. The proposed approach is applied to a simple test model. It is shown that the magnetic flux density distributions can be obtained without solving finite element equations and using backpropagation learning.

本誌: 2023年3月2日-2023年3月3日静止器/回転機合同研究会-2

本誌掲載ページ: 27-32 p

原稿種別: 日本語

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