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Paper Abstract and Keywords
Presentation 2026-07-16 16:20
A Study on Causality in Time-Domain Electromagnetic Analysis Using PINN With Causal Training
Yuki Yanagisawa, Tadatoshi Sekine (Shizuoka Univ.) EMCJ2026-23
Abstract (in Japanese) (See Japanese page) 
(in English) In this report, we focus on the causality of physics-informed neural network (PINN) in the time-domain electromagnetic analysis and validate the effectiveness of the application of causal training, which is a training method considering the causality, to the PINN. Here, the causality refers to the property that no variation in the electromagnetic field distribution occurs before excitation. In our verification, we evaluate effects on the causality by showing numerical results in the cases of a regular temporal sampling and the causal training applied to the PINN. As a result, in both cases of the regular sampling and PINN with the causal training, it has been confirmed that the causality is improved as non-physical electric field variation occurred in the conventional PINN is suppressed.
Keyword (in Japanese) (See Japanese page) 
(in English) Electromagnetic Field Analysis / Causality / Causal Training / Physics-Informed Neural Network (PINN) / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 114, EMCJ2026-23, pp. 41-46, July 2026.
Paper # EMCJ2026-23 
Date of Issue 2026-07-09 (EMCJ) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF EMCJ2026-23

Conference Information
Committee EMCJ  
Conference Date 2026-07-16 - 2026-07-16 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To EMCJ 
Conference Code 2026-07-EMCJ 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Causality in Time-Domain Electromagnetic Analysis Using PINN With Causal Training 
Sub Title (in English)  
Keyword(1) Electromagnetic Field Analysis  
Keyword(2) Causality  
Keyword(3) Causal Training  
Keyword(4) Physics-Informed Neural Network (PINN)  
Keyword(5)  
Keyword(6)  
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Keyword(8)  
1st Author's Name Yuki Yanagisawa  
1st Author's Affiliation Shizuoka University (Shizuoka Univ.)
2nd Author's Name Tadatoshi Sekine  
2nd Author's Affiliation Shizuoka University (Shizuoka Univ.)
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Speaker Author-1 
Date Time 2026-07-16 16:20:00 
Presentation Time 25 minutes 
Registration for EMCJ 
Paper # EMCJ2026-23 
Volume (vol) vol.126 
Number (no) no.114 
Page pp.41-46 
#Pages 6 
Date of Issue 2026-07-09 (EMCJ) 

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