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Paper Abstract and Keywords
Presentation 2026-08-07 16:00
Predicting bifurcations in dynamical systems with transfer learning
Lingfeng Chen (TUS), Masanobu Inubushi (TUS/UOsaka) CCS2026-36
Abstract (in Japanese) (See Japanese page) 
(in English) Predicting bifurcations in dynamical systems is an important challenge for understanding and controlling nonlinear phenomena. In this study, we apply a transfer learning approach based on Reservoir Computing (RC) to the prediction of bifurcations in dynamical systems. Specifically, we propose a bifurcation prediction method that extrapolates beyond the range of training parameters by linearly combining two output layers trained independently at different parameter values. Using the Lorenz63 system as a benchmark, we train the model solely on trajectories converging to fixed points before the Hopf bifurcation and successfully reconstruct the chaotic attractor after the bifurcation. Furthermore, by introducing a quantitative evaluation metric, we demonstrate that the proposed method achieves prediction accuracy comparable to or better than that of the conventional approach. We also verify that the proposed method reconstructs the bifurcation diagrams of saddle-node and pitchfork bifurcations by tracking fixed points, demonstrating its effectiveness for predicting representative bifurcation phenomena.
Keyword (in Japanese) (See Japanese page) 
(in English) Dynamical System / Predicting bifurcations / Reservoir Computing / Transfer Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 143, CCS2026-36, pp. 55-60, Aug. 2026.
Paper # CCS2026-36 
Date of Issue 2026-07-30 (CCS) 
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 CCS2026-36

Conference Information
Committee IN CCS  
Conference Date 2026-08-06 - 2026-08-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Sapporo Educaton and Culture Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network Science, Future Network, Cloud/SDN/Virtualization, Contents Delivery/Contents Exchange, and others 
Paper Information
Registration To CCS 
Conference Code 2026-08-IN-CCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Predicting bifurcations in dynamical systems with transfer learning 
Sub Title (in English)  
Keyword(1) Dynamical System  
Keyword(2) Predicting bifurcations  
Keyword(3) Reservoir Computing  
Keyword(4) Transfer Learning  
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1st Author's Name Lingfeng Chen  
1st Author's Affiliation Tokyo University of Science (TUS)
2nd Author's Name Masanobu Inubushi  
2nd Author's Affiliation Tokyo University of Science/The University of Osaka (TUS/UOsaka)
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Speaker Author-1 
Date Time 2026-08-07 16:00:00 
Presentation Time 25 minutes 
Registration for CCS 
Paper # CCS2026-36 
Volume (vol) vol.126 
Number (no) no.143 
Page pp.55-60 
#Pages 6 
Date of Issue 2026-07-30 (CCS) 

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