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| Paper Abstract and Keywords | ||
| Presentation | 2022-11-24 16:15 Genetic programming supported by physics-inspired methods Soichiro Kanaya, Toma Takano, Satoshi Sunada, Tomoaki Niiyama (Kanazawa Univ.)  NLP2022-66 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We study a symbolic regression technique to infer the equations of systems from the observed numerical data. Our method is based on the AI-Feynman, proposed by Udrescu et al., which uses neural networks to detect features of the data, and genetic programming, which is an efficient formula search method that mimics biological evolution. In this study, we show that our method can successfully infer simple equations from measurement data with the aid of the AI-Feynman. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | AI-Feynman / Symbolic regression / Genetic programming / Neural network / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 280, NLP2022-66, pp. 42-42, Nov. 2022. | |
| Paper # | NLP2022-66 | |
| Date of Issue | 2022-11-17 (NLP) | |
| 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 | NLP2022-66 |
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| Conference Information | |
| Committee | NLP |
| Conference Date | 2022-11-24 - 2022-11-25 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NLP |
| Conference Code | 2022-11-NLP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Genetic programming supported by physics-inspired methods |
| Sub Title (in English) | |
| Keyword(1) | AI-Feynman |
| Keyword(2) | Symbolic regression |
| Keyword(3) | Genetic programming |
| Keyword(4) | Neural network |
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| 1st Author's Name | Soichiro Kanaya |
| 1st Author's Affiliation | Kanazawa University (Kanazawa Univ.) |
| 2nd Author's Name | Toma Takano |
| 2nd Author's Affiliation | Kanazawa University (Kanazawa Univ.) |
| 3rd Author's Name | Satoshi Sunada |
| 3rd Author's Affiliation | Kanazawa University (Kanazawa Univ.) |
| 4th Author's Name | Tomoaki Niiyama |
| 4th Author's Affiliation | Kanazawa University (Kanazawa Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-11-24 16:15:00 |
| Presentation Time | 25 minutes |
| Registration for | NLP |
| Paper # | NLP2022-66 |
| Volume (vol) | vol.122 |
| Number (no) | no.280 |
| Page | p.42 |
| #Pages | 1 |
| Date of Issue | 2022-11-17 (NLP) |
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