000128131 001__ 128131 000128131 005__ 20241125101145.0 000128131 0247_ $$2doi$$a10.1063/5.0143876 000128131 0248_ $$2sideral$$a135159 000128131 037__ $$aART-2023-135159 000128131 041__ $$aeng 000128131 100__ $$0(orcid)0000-0002-8089-343X$$aBarrio, Roberto$$uUniversidad de Zaragoza 000128131 245__ $$aDeep learning for chaos detection 000128131 260__ $$c2023 000128131 5060_ $$aAccess copy available to the general public$$fUnrestricted 000128131 5203_ $$aIn this article, we study how a chaos detection problem can be solved using Deep Learning techniques. We consider two classical test examples: the Logistic map as a discrete dynamical system and the Lorenz system as a continuous dynamical system. We train three types of artificial neural networks (multi-layer perceptron, convolutional neural network, and long short-term memory cell) to classify time series from the mentioned systems into regular or chaotic. This approach allows us to study biparametric and triparametric regions in the Lorenz system due to their low computational cost compared to traditional techniques. 000128131 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2021-122961NB-I00$$9info:eu-repo/grantAgreement/ES/DGA/E22-20R$$9info:eu-repo/grantAgreement/ES/DGA-FSE/E24-20R$$9info:eu-repo/grantAgreement/ES/DGA/LMP94_21$$9info:eu-repo/grantAgreement/ES/DGA/T36-20R$$9info:eu-repo/grantAgreement/ES/MCIU/FPU20-04039$$9info:eu-repo/grantAgreement/ES/MICINN/PGC2018-096026-B-I00 000128131 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000128131 590__ $$a2.7$$b2023 000128131 592__ $$a0.778$$b2023 000128131 591__ $$aPHYSICS, MATHEMATICAL$$b5 / 60 = 0.083$$c2023$$dQ1$$eT1 000128131 593__ $$aPhysics and Astronomy (miscellaneous)$$c2023$$dQ1 000128131 591__ $$aMATHEMATICS, APPLIED$$b25 / 332 = 0.075$$c2023$$dQ1$$eT1 000128131 593__ $$aMathematical Physics$$c2023$$dQ2 000128131 593__ $$aStatistical and Nonlinear Physics$$c2023$$dQ2 000128131 593__ $$aApplied Mathematics$$c2023$$dQ2 000128131 593__ $$aMedicine (miscellaneous)$$c2023$$dQ2 000128131 594__ $$a5.2$$b2023 000128131 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000128131 700__ $$0(orcid)0000-0002-1184-5901$$aLozano, Álvaro$$uUniversidad de Zaragoza 000128131 700__ $$0(orcid)0000-0002-4802-2511$$aMayora-Cebollero, Ana$$uUniversidad de Zaragoza 000128131 700__ $$0(orcid)0000-0002-3431-0926$$aMayora-Cebollero, Carmen$$uUniversidad de Zaragoza 000128131 700__ $$0(orcid)0000-0001-5803-4316$$aMiguel, Antonio$$uUniversidad de Zaragoza 000128131 700__ $$0(orcid)0000-0002-3886-7748$$aOrtega, Alfonso$$uUniversidad de Zaragoza 000128131 700__ $$0(orcid)0000-0002-5701-1670$$aSerrano, Sergio$$uUniversidad de Zaragoza 000128131 700__ $$0(orcid)0000-0001-7111-5022$$aVigara, Rubén$$uUniversidad de Zaragoza 000128131 7102_ $$12006$$2440$$aUniversidad de Zaragoza$$bDpto. Matemáticas$$cÁrea Geometría y Topología 000128131 7102_ $$12005$$2595$$aUniversidad de Zaragoza$$bDpto. Matemática Aplicada$$cÁrea Matemática Aplicada 000128131 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac. 000128131 773__ $$g33, 7 (2023), 073146 [50 pp.]$$pChaos$$tCHAOS$$x1054-1500 000128131 8564_ $$s241833$$uhttps://zaguan.unizar.es/record/128131/files/texto_completo.pdf$$yPostprint 000128131 8564_ $$s819570$$uhttps://zaguan.unizar.es/record/128131/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000128131 909CO $$ooai:zaguan.unizar.es:128131$$particulos$$pdriver 000128131 951__ $$a2024-11-22-12:04:04 000128131 980__ $$aARTICLE