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Lee, Woo Seok
복잡계 이론물리 연구단
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Deep learning of chaos classification

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dc.contributor.authorWoo Seok Lee-
dc.contributor.authorSergej Flach-
dc.date.accessioned2021-01-12T02:30:02Z-
dc.date.accessioned2021-01-12T02:30:02Z-
dc.date.available2021-01-12T02:30:02Z-
dc.date.available2021-01-12T02:30:02Z-
dc.date.created2020-11-10-
dc.date.issued2020-12-
dc.identifier.issn2632-2153-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/9034-
dc.description.abstract© 2020 The Author(s). Published by IOP Publishing Ltd. We train an artificial neural network which distinguishes chaotic and regular dynamics of the two-dimensional Chirikov standard map. We use finite length trajectories and compare the performance with traditional numerical methods which need to evaluate the Lyapunov exponent (LE). The neural network has superior performance for short periods with length down to 10 Lyapunov times on which the traditional LE computation is far from converging. We show the robustness of the neural network to varying control parameters, in particular we train with one set of control parameters, and successfully test in a complementary set. Furthermore, we use the neural network to successfully test the dynamics of discrete maps in different dimensions, e.g. the one-dimensional logistic map and a three-dimensional discrete version of the Lorenz system. Our results demonstrate that a convolutional neural network can be used as an excellent chaos indicator.-
dc.description.uri1-
dc.language영어-
dc.publisherIOP Publishing-
dc.titleDeep learning of chaos classification-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.rimsid73628-
dc.contributor.affiliatedAuthorWoo Seok Lee-
dc.contributor.affiliatedAuthorSergej Flach-
dc.identifier.doi10.1088/2632-2153/abb6d3-
dc.identifier.bibliographicCitationMachine Learning: Science and Technology, v.1, no.4-
dc.citation.titleMachine Learning: Science and Technology-
dc.citation.volume1-
dc.citation.number4-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
Appears in Collections:
Center for Theoretical Physics of Complex Systems(복잡계 이론물리 연구단) > 1. Journal Papers (저널논문)
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