dc.citation.conferencePlace |
JA |
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dc.citation.conferencePlace |
도쿄 와세다 대학교 |
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dc.citation.title |
10th International Council for Industrial and Applied Mathematics (ICIAM 2023 TOKYO) |
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dc.contributor.author |
Chio, Junhyeok |
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dc.contributor.author |
Jang, Bongsoo |
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dc.date.accessioned |
2024-01-05T17:05:16Z |
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dc.date.available |
2024-01-05T17:05:16Z |
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dc.date.created |
2024-01-05 |
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dc.date.issued |
2023-08-23 |
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dc.description.abstract |
We propose a new method using a deep neural network (DNN) to predict extinction in a cyclic competition model, which is motivated by the relationship between pattern formation and mobility. Numerical experiments showed that DNN performed well except for intermediate mobility, where the extinction probability increases sharply. Therefore, we suggest a hybrid method combining with Monte Carlo and DNN, whose computational cost reduces remarkably compared with the conventional method. |
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dc.identifier.bibliographicCitation |
10th International Council for Industrial and Applied Mathematics (ICIAM 2023 TOKYO) |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/67735 |
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dc.language |
영어 |
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dc.publisher |
International Council for Industrial and Applied Mathematics |
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dc.title |
Analyzing the structure of cyclical competition using deep learning method |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2023-08-20 |
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