000150519 001__ 150519
000150519 005__ 20251017144641.0
000150519 0247_ $$2doi$$a10.1016/j.physd.2024.134510
000150519 0248_ $$2sideral$$a142580
000150519 037__ $$aART-2024-142580
000150519 041__ $$aeng
000150519 100__ $$0(orcid)0000-0002-3431-0926$$aMayora-Cebollero, Carmen$$uUniversidad de Zaragoza
000150519 245__ $$aFull Lyapunov exponents spectrum with Deep Learning from single-variable time series
000150519 260__ $$c2024
000150519 5060_ $$aAccess copy available to the general public$$fUnrestricted
000150519 5203_ $$aIn this article we study if a Deep Learning technique can be used to obtain an approximate value of the Lyapunov exponents of a dynamical system. Moreover, we want to know if Machine Learning techniques are able, once trained, to provide the full Lyapunov exponents spectrum with just single-variable time series. We train a Convolutional Neural Network and use the resulting network to approximate the full spectrum using the time series of just one variable from the studied systems (Lorenz system and coupled Lorenz system). The results are quite surprising since all the values are well approximated with only partial data. This strategy allows to speed up the complete analysis of the systems and also to study the hyperchaotic dynamics in the coupled Lorenz system.
000150519 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2021-122961NB-I00$$9info:eu-repo/grantAgreement/ES/DGA/E22-23R$$9info:eu-repo/grantAgreement/ES/DGA/E24-23R$$9info:eu-repo/grantAgreement/ES/DGA/LMP94_21
000150519 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000150519 590__ $$a2.9$$b2024
000150519 592__ $$a0.94$$b2024
000150519 591__ $$aMATHEMATICS, APPLIED$$b22 / 343 = 0.064$$c2024$$dQ1$$eT1
000150519 593__ $$aApplied Mathematics$$c2024$$dQ1
000150519 591__ $$aPHYSICS, FLUIDS & PLASMAS$$b9 / 41 = 0.22$$c2024$$dQ1$$eT1
000150519 593__ $$aCondensed Matter Physics$$c2024$$dQ1
000150519 591__ $$aPHYSICS, MATHEMATICAL$$b8 / 61 = 0.131$$c2024$$dQ1$$eT1
000150519 593__ $$aMathematical Physics$$c2024$$dQ1
000150519 591__ $$aPHYSICS, MULTIDISCIPLINARY$$b35 / 114 = 0.307$$c2024$$dQ2$$eT1
000150519 593__ $$aStatistical and Nonlinear Physics$$c2024$$dQ2
000150519 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000150519 700__ $$0(orcid)0000-0002-4802-2511$$aMayora-Cebollero, Ana$$uUniversidad de Zaragoza
000150519 700__ $$0(orcid)0000-0002-1184-5901$$aLozano, Álvaro$$uUniversidad de Zaragoza
000150519 700__ $$0(orcid)0000-0002-8089-343X$$aBarrio, Roberto$$uUniversidad de Zaragoza
000150519 7102_ $$12006$$2440$$aUniversidad de Zaragoza$$bDpto. Matemáticas$$cÁrea Geometría y Topología
000150519 7102_ $$12005$$2595$$aUniversidad de Zaragoza$$bDpto. Matemática Aplicada$$cÁrea Matemática Aplicada
000150519 773__ $$g472 (2024), 134510 [17 pp.]$$pPhysica, D$$tPHYSICA D-NONLINEAR PHENOMENA$$x0167-2789
000150519 8564_ $$s7770874$$uhttps://zaguan.unizar.es/record/150519/files/texto_completo.pdf$$yVersión publicada
000150519 8564_ $$s2454912$$uhttps://zaguan.unizar.es/record/150519/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000150519 909CO $$ooai:zaguan.unizar.es:150519$$particulos$$pdriver
000150519 951__ $$a2025-10-17-14:32:23
000150519 980__ $$aARTICLE