Orientador: Marcus Aloizio Martinez de Aguiar / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Fisica Gleb Wataghin / Made available in DSpace on 2018-08-06T21:05:02Z (GMT). No. of bitstreams: 1 Bonanca_MarcusViniciusSegantini_D.pdf: 10284922 bytes, checksum: 28ea976c05e0eadcda732211e40afb25 (MD5) Previous issue date: 2006 / Resumo: Neste trabalho, estudamos de que maneira e sob que condições um sistema caótico com apenas dois graus de liberdade produz efeitos irreversíveis como dissipação, termalização e, do ponto de vista quântico, perda de coerência em um sistema simples a ele acoplado. Na formulação clássica do problema, descrevemos analiticamente o comportamento do fluxo de energia em Resposta Linear e apontamos o ingrediente talvez principal que um sistema caótico possui para causar irreversibilidade: correlações que decaem exponencialmente. Mostramos que é possível descrever o equilíbrio assintótico inclusive com uma temperatura, o que é não-intuitivo em se tratando de sistemas pequenos. Esse último resultado completa o paralelo entre o movimento Browniano usual e o modelo proposto. Formulamos o problema do ponto de vista quântico via o formalismo de Funcionais de Influência. Mostramos que este formalismo é mesmo adequado pois a influência do sistema caótico é descrita pelas contrapartidas quânticas das mesmas funções que encontramos na Resposta Linear clássica. Calculamos semiclassicamente essas funções e mostramos que os termos em mais baixa ordem da aproximação semiclássica evoluem conforme a dinâmica clássica caótica. As escalas de tempo da análise clássica se mostram fundamentais para a resolução dos cálculos assim como a análise semiclássica das funções de correlação. Mostramos que efeitos de dissipação e perda de coerência, no contexto quântico, são possíveis devido ao caráter caótico do sistema / Abstract: We study here how and under which conditions a chaotic system with only two degrees of freedom can produce irreversible phenomena such as dissipation, thermalization and, from the quantum point of view, decoherence in a simple system coupled to it. In the classical formulation of the problem, we describe analytically the behavior of the energy ux in Linear Response regime and we point the main ingredient for a chaotic system to produce irreversible effects: correlations with exponential decay. We show that it is possible to describe the asymptotic equilibrium even with a temperature, which seems to be a counter intuitive result for systems with few degrees of freedom. We formulate the problem from the quantum point of view using In uence Functionals approach. We show the formalism is very adequate since the chaotic system in uence is described by quantum analogues of the same functions we obtain in the Linear Response approach to the classical problem. We calculate those functions semiclassically and we show the lowest order terms of the semiclassical approximation evolve as given by classical chaotic dynamics. The time scales of the classical analysis are shown to be very important for the resolution of the quantum problem as well as the semiclassical analysis of the correlation functions. We show that dissipative and decoherence effects, in the quantum regime, are possible due to the chaotic dynamics of the system / Doutorado / Física Estatistica e Termodinamica / Doutor em Ciências

No description available.

Dans ce travail de thèse, nous développons des méthodes de synchronisation des systèmes chaotiques pour les applications de transmission d'informations. La première méthode de synchronisation que nous proposons est basée sur les observateurs adaptatifs à entrées inconnues pour une classe des systèmes chaotiques présentant des incertitudes paramétriques et des perturbations dans leurs dynamiques et du bruit dans les signaux de sortie (bruit dans le canal de communication). La méthode développée repose sur les techniques adaptatives pour la compensation des non-linéarités et des incertitudes paramétriques et pour la restauration des messages transmis. Elle se base également sur les méthodes de synthèse d'observateurs à entrées inconnues pour supprimer l'influence des perturbations et du bruit. Ensuite, nous développons une deuxième méthode de synchronisation utilisant un observateur adaptatif à ``modes glissants" pour une classe des systèmes chaotiques présentant des entrées inconnues et dont les signaux de sortie sont bruités. La synthèse de l'observateur s'appuie sur la théorie des modes glissants, les techniques de synthèse d'observateurs singuliers et les techniques adaptatives dans le but d'estimer conjointement l'état et les entrées inconnues malgré la présence du bruit dans les équations de sortie. Cette approche de synchronisation est ensuite employée dans un nouveau schéma de communication chaotique sécurisée dont l'objectif est d'augmenter le nombre et l'amplitude des messages transmis, améliorer le niveau de sécurité ainsi que la robustesse aux bruits présents dans le canal de communication. En outre, le scénario de présence des retards de transmission est étudié en élaborant une troisième approche de synchronisation à base d'observateurs adaptatifs pour une classe des systèmes chaotiques de Lur'e avec des non-linéarités à pente restreinte et des signaux de sortie retardés. En se basant sur la théorie de Lyapunov-Krasovskii et en utilisant une hypothèse d'excitation persistante, l'observateur adaptatif proposé garantit la synchronisation maitre-esclave et la restauration des informations transmises malgré l'existence des retards de transmission. Les résultats théoriques obtenus dans ce travail de thèse sont vérifiés à travers des applications de transmission d'informations utilisant différents modèles des systèmes chaotiques tout en étudiant les différents scénarios et cas de figure pouvant se présenter en pratique et en analysant les aspects de sécurité de ces systèmes. / In this thesis, we develop synchronization methods of chaotic systems for information transmission applications. The first proposed method is based on adaptive unknown input observers for a class of chaotic systems subject to parametric uncertainties and perturbations in their dynamics and noise in outputs signals (Channel communication noise). The developed method is based on adaptive techniques to compensate nonlinearities to compensate nonlinearities and parametric uncertainties and to reconstruct the transmitted messages. Furthermore, this approach is based on unknown input observers design to reject the influence of perturbations and noise. Then, we develop a second synchronization method using an adaptive ``sliding mode” observer for a class of chaotic systems subject to unknown inputs and such that the output equations are subject to noise. The observer design is based on sliding modes theory, descriptor observers design and adaptive control in order to join state and unknown input estimation despite the presence of noise in output equations. The latter synchronization approach is then exploited in a new secured communication scheme where the objective is to increase the number and amplitude of the transmitted messages, improve the level of security and the robustness to noise present in the communication channel. Moreover, the case of presence of transmission time-delays was investigated and a synchronization approach based on adaptive observers for a class of Lur’e systems with slope restricted nonlinearities and delayed outputs. Based on the Lyapunov-Krasovskii theory and using a persistency of excitation property, the proposed adaptive observer ensures master-slave synchronization and the reconstruction of the transmitted messages despite the existence of transmission time-delays. The obtained theoretical results in this thesis are verified through transmission information applications using different models of chaotic systems in different scenarios and case-studies which may occur in practice. Cryptanalysis and security aspects of the proposed communication systems are also investigated.

De façon générale, cette thèse porte sur l'estimation de l'état et des entrées inconnues pour une classe de systèmes non linéaires. De façon plus particulière, le problème est abordé sous l'angle de la conception d'un système de transmission sécurisée d'informations exploitant les propriétés des systèmes chaotiques et leur capacité de synchronisation. Les travaux présentés traitent trois points principaux, à savoir le choix de l'émetteur, le développement du récepteur, et la mise au point du processus de transmission de l'information ou du message. L'émetteur retenu est un système non linéaire chaotique dont la dynamique comporte un retard, ce qui lui confère un comportement particulièrement complexe. La conception du récepteur repose sur la synthèse d'un observateur non linéaire, dont la stabilité et la convergence garantissent la synchronisation avec l'émetteur. L'insertion du message est réalisée par modulation de la phase d'un signal porteur chaotique. Le décryptage de l'information s'apparente à une restauration d'entrée inconnue au niveau du récepteur. Une étude de la sécurité du processus de cryptage/décryptage a été menée, reposant sur des techniques standard de cryptanalyse. Des multimodèles chaotiques ont été proposés pour renforcer la sécurité du processus de synchronisation / In a general way, this thesis deals with state and unknown input estimation for a class of nonlinear systems. In a more particular way, the problem is addressed from a secure communication system design point of view, based on chaotic systems properties and synchronization ability. Our work deals with three main points: selection of the transmitter, design of the receiver, and development of the information (or message) transmission process. The chosen transmitter is a time-delay nonlinear chaotic system: the main reason is that a very complex behavior is brought about by the delayed state feedback. The receiver design relies on a nonlinear observer synthesis, whose stability and convergence ensure synchronization with the transmitter. The message insertion is realized through a chaotic carrier phase modulation. The decryption process is similar to an unknown input recovery, at the receiver side. The security of the proposed encryption/decryption process is studied using standard cryptanalysis techniques. Chaotic multimodels are defined to tighten up the synchronization process security

Neste trabalho, redes neurais dinÃmicas sÃo avaliadas como modelos nÃo-lineares eficientes para prediÃÃo de sÃries temporais complexas. Entre as arquiteturas avaliadas estÃo as redes FTDNN, Elman e NARX. A capacidade preditiva destas redes sÃo testadas em tarefas de prediÃÃo de um-passo-adiante e mÃltiplos-passos-adiante. Para este fim, sÃo usadas as seguintes sÃries temporais: sÃrie laser caÃtico, sÃrie caÃtica Mackey-Glass, alÃm de sÃries de trÃfego de rede de computadores com caracterÃsticas auto-similares. O uso da rede NARX em prediÃÃo de sÃries temporais à uma contribuiÃÃo desta dissertaÃÃo. Esta rede possui uma arquitetura neural recorrente usada originalmente para identificaÃÃo entrada-saÃda de sistemas nÃo-lineares. A entrada da rede NARX à formada por duas janelas deslizantes (sliding time window), uma que desliza sobre o sinal de entrada e outra que desliza sobre sinal de saÃda. Quando aplicada para prediÃÃo caÃtica de sÃries temporais, a rede NARX à projetada geralmente como um modelo autoregressivo nÃolinear (NAR), eliminando a janela de atraso da saÃda. Neste trabalho, à proposta uma estratÃgia simples, porÃm eficiente, para permitir que a rede NARX explore inteiramente as janelas de tempo da entrada e da saÃda, a fim de melhorar sua capacidade preditiva. Os resultados obtidos mostram que a abordagem proposta tem desempenho superior ao desempenho apresentado por preditores baseados nas redes FTDNN e Elman.

In this essay I defend the notion of deductive explanation mainly against two types of putative counterexamples: those found in genuinely indeterministic systems and those found in complex dynamic systems. Using Railton's notions of explanatory information and ideal explanatory text, deductivism is defended in an indeterministic setting. Furthermore, an argument against non-deductivism that hinges on peculiarities of probabilistic causality is presented. The use of the notion of an ideal explanatory text gives rise to problems in accounting for explanations in complex dynamic systems, regardless of whether they are deterministic or not. These problems are considered in the essay and a solution is suggested. This solution forces the deductivist to abandon the requirement that an explanation consists of a deductive argument, but it is argued that the core of deductivism is saved in so far as we, for full explanations, can still adhere to the fundamental requirement: If A explains B, then A is inconsistent with anything inconsistent with B.

Submitted by Alisson de Carvalho Reinol null (alissoncarv@gmail.com) on 2017-07-18T15:03:51Z No. of bitstreams: 1 tese_alisson_final.pdf: 6086108 bytes, checksum: 610534618b19a1d27cfff678d44f1a4a (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-07-19T14:22:46Z (GMT) No. of bitstreams: 1 reinol_ac_dr_sjrp.pdf: 6086108 bytes, checksum: 610534618b19a1d27cfff678d44f1a4a (MD5) / Made available in DSpace on 2017-07-19T14:22:46Z (GMT). No. of bitstreams: 1 reinol_ac_dr_sjrp.pdf: 6086108 bytes, checksum: 610534618b19a1d27cfff678d44f1a4a (MD5) Previous issue date: 2017-07-05 / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Neste trabalho, consideramos aspectos algébricos e dinâmicos de alguns problemas envolvendo superfícies algébricas invariantes em sistemas diferenciais polinomiais definidos em R³. Determinamos o número máximo de planos invariantes que um sistema diferencial quadrático pode ter e estudamos a realização e integrabilidade de tais sistemas. Fornecemos a forma normal para sistemas diferenciais com quádricas invariantes e estudamos de forma mais detalhada a dinâmica e integrabilidade de sistemas diferenciais quadráticos com um paraboloide elíptico como superfície algébrica invariante. Por fim, estudamos as consequências dinâmicas ao se perturbar um sistema diferencial, cujo espaço de fase é folheado por superfícies algébricas invariantes. Para tal, consideramos o sistema diferencial quadrático conhecido como sistema Sprott A, que depende de um parâmetro real a e apresenta comportamento caótico mesmo sem ter pontos de equilíbrio, tendo, assim, um hidden attractor para valores adequados do parâmetro a. Provamos que, para a=0, o espaço de fase desse sistema é folheado por esferas concêntricas invariantes. Utilizando a Teoria do Averaging e o Teorema KAM (Kolmogorov-Arnold-Moser), provamos que, para a>0 suficientemente pequeno, uma órbita periódica orbitalmente estável emerge de um equilíbrio do tipo zero-Hopf não isolado localizado na origem e que formam-se toros invariantes em torno desta órbita periódica. Concluímos que a ocorrência de tais fatos tem um papel importante na formação do hidden attractor. / In this work, we consider algebraic and dynamical aspects of some problems involving invariant algebraic surfaces in polynomial differential systems defined in R³. We determine the maximum number of invariant planes that a quadratic differential system can have and we study the realization and integrability of such systems. We provide the normal form for differential systems having an invariant quadric and we study in more detail the dynamics and integrability of quadratic differential systems having an elliptic paraboloid as invariant algebraic surface. Finally, we study the dynamic consequences of perturbing differential system whose phase space is foliated by invariant algebraic surfaces. For this we consider the quadratic differential system known as Sprott A system, which depends on one real parameter a and presents chaotic behavior even without having any equilibrium point, thus having a hidden attractor for suitable values of parameter a. We prove that, for a=0, the phase space of this system is foliated by invariant concentric spheres. By using the Averaging Theory and the KAM (Kolmogorov-Arnold-Moser) Theorem, we prove that, for a>0 sufficiently small, an orbitally stable periodic orbit emerges from a zero-Hopf nonisolated equilibrium point located at the origin and that invariant tori are formed around this periodic orbit. We conclude that the occurrence of these facts has an important role in the formation of the hidden attractor. / FAPESP: 2013/26602-7

L’objectif de cette thèse était de développer une méthode de synthèse de commande par retour d’état puis par observateurs offrant des conditions de synthèse non contraignantes dans le cas de systèmes non linéaires à temps discret. Dans cette méthode, est mise en exergue l’importance du choix de la description des systèmes sur l’étendue des résultats pouvant être obtenus lorsque la méthode d’étude de la stabilité est fixée. Ainsi l’utilisation des normes vectorielles comme fonction d’agrégation et du critère pratique de Borne et Gentina pour l’étude de la stabilité, associée à la description des systèmes par des matrices caractéristiques de forme en flèche de Benrejeb, a conduit à l’élaboration de nouvelles conditions suffisantes de stabilisation de systèmes dynamiques discrets non linéaires, formulées en théorèmes et corollaires. Ces résultats obtenus, sont ensuite exploités, avec succès, pour la formulation de nouvelles conditions suffisantes de vérification des propriétés de synchronisation pour les systèmes hyperchaotiques à temps discrets. Ensuite, le cas de synthèse d’observateur est validé dans deux types de transmission chaotique / The objective of this thesis was to develop a method for synthesizing control state feedback and observers by offering soft synthesis conditions in the case of nonlinear discrete-time systems. In this method, is highlighting the importance of choosing the systems description of the scope of what can be achieved when the stability study method is fixed. The use of of vector norms as an aggregation function and the practical Borne-Gentina criterion for stability study, associated to arrow form matrix of Benrejeb for system discription, lead to the development of new sufficient conditions for stabilization of nonlinear discrete dynamical systems, formulated as theorems and corollaries. These results are then used, with success, for the formulation of new sufficient conditions for checking properties of hyperchaotiques synchronization for discrete-time systems. Then, the synthesis of observer is validated in two types of chaotic transmission

No description available.

Tesis por compendio / [ES] Durante las últimas décadas el uso del aprendizaje automático (machine learning) y de la inteligencia artificial ha mostrado un crecimiento exponencial en muchas áreas de la ciencia. El hecho de que los ordenadores hayan aumentado sus restaciones a la vez que han reducido su precio, junto con la disponibilidad de entornos de desarrollo de código abierto han permitido el acceso a la inteligencia artificial a un gran rango de investigadores, democratizando de esta forma el acceso a métodos de inteligencia artificial a la comunidad investigadora. Es nuestra creencia que la multidisciplinaridad es clave para nuevos logros, con equipos compuestos de investigadores con diferentes preparaciones y de diferentes campos de especialización. Con este ánimo, hemos orientado esta tesis en el uso de machine learning inteligencia artificial, aprendizaje profundo o deep learning, entendiendo todas las anteriores como parte de un concepto global que concretamos en el término inteligencia artificial, a intentar arrojar luz a algunos problemas de los campos de las matemáticas y la física. Desarrollamos una arquitectura deep learning y la medimos con éxito en la caracterización de procesos de difusión anómala. Mientras que previamente se habían utilizado métodos estadísticos clásicos con este objetivo, los métodos de deep learning han demostrado mejorar las prestaciones de dichos métodos clásicos. Nuestra architectura demostró que puede inferir con precisión el exponente de difusión anómala y clasificar trayectorias entre un conjunto dado de modelos subyacentes de difusión . Mientras que las redes neuronales recurrentes irrumpieron recientemente, los modelos basados en redes convolucionales han sido ámpliamente testados en el campo del procesamiento de imagen durante más de 15 años. Existen muchos modelos y arquitecturas, pre-entrenados y listos para ser usados por la comunidad. No es necesario realizar investigación ya que dichos modelos han probado su valía durante años y están bien documentados en la literatura. Nuestro objetivo era ser capaces de usar esos modelos bien conocidos y fiables, con trayectorias de difusión anómala. Solo necesitábamos convertir una serie temporal en una imagen, cosa que hicimos aplicando gramian angular fields a las trayectorias, poniendo el foco en las trayectorias cortas. Hasta donde sabemos, ésta es la primera vez que dichas técnicas son usadas en este campo. Mostramos cómo esta aproximación mejora las prestaciones de cualquier otra propuesta en la clasificación del modelo subyacente de difusión anómala para trayectorias cortas. Más allá de la física están las matemáticas. Utilizamos nuestra arquitectura basada en redes recurrentes neuronales para inferir los parámetros que definen las trayectorias de Wu Baleanu. Mostramos que nuestra propuesta puede inferir con azonable precisión los parámetros mu y nu. Siendo la primera vez, de nuevo hasta donde llega nuestro conocimiento, que tales técnicas se aplican en este escenario. Extendemos este trabajo a las ecuaciones fraccionales discretas con retardo, obteniendo resultados similares en términos de precisión. Adicionalmente, mostramos que la misma arquitectura se puede usar para discriminar entre trayectorias con y sin retardo con gran confianza. Finalmente, también investigamos modelos fraccionales discretos. Hemos analizado esquemas de paso temporal con la cuadratura de Lubich en lugar del clásico esquema de orden 1 de Euler. En el primer estudio de este nuevo paradigma hemos comparado los diagramas de bifurcación de los mapas logístico y del seno, obtenidos de la discretización de Euler de orden 1, 2 y 1/2. / [CAT] Durant les darreres dècades l'ús de l'aprenentatge automàtic (machine learning) i de la intel.ligència artificial ha mostrat un creixement exponencial en moltes àrees de la ciència. El fet que els ordinadors hagen augmentat les seues prestacions a la vegada que han reduït el seu preu, junt amb la disponibilitat d'entorns de desenvolupament de codi obert han permès l'accés a la intel.ligència artificial a un gran rang d'investigadors, democratitzant així l'accés a mètodes d'intel.ligència artificial a la comunitat investigadora. És la nostra creença que la multidisciplinaritat és clau per a nous èxits, amb equips compostos d'investigadors amb diferents preparacions i diferents camps d'especialització. Amb aquest ànim, hem orientat aquesta tesi en l'ús d'intel.ligència artificial machine learning, aprenentatge profund o deep learning, entenent totes les anteriors com a part d'un concepte global que concretem en el terme intel.ligència, a intentar donar llum a alguns problemes dels camps de les matemàtiques i la física. Desenvolupem una arquitectura deep learning i la mesurem amb èxit en la caracterització de processos de difusió anòmala. Mentre que prèviament s'havien utilitzat mètodes estadístics clàssics amb aquest objectiu, els mètodes de deep learning han demostrat millorar les prestacions d'aquests mètodes clàssics. La nostra architectura va demostrar que pot inferir amb precisió l'exponent de difusió anòmala i classificar trajectòries entre un conjunt donat de models subjacents de difusió. Mentre que les xarxes neuronals recurrents van irrompre recentment, els models basats en xarxes convolucionals han estat àmpliament testats al camp del processament d'imatge durant més de 15 anys. Hi ha molts models i arquitectures, pre-entrenats i llestos per ser usats per la comunitat. No cal fer recerca ja que aquests models han provat la seva vàlua durant anys i estan ben documentats a la literatura. El nostre objectiu era ser capaços de fer servir aquests models ben coneguts i fiables, amb trajectòries de difusió anòmala. Només necessitàvem convertir una sèrie temporal en una imatge, cosa que vam fer aplicant gramian angular fields a les trajectòries, posant el focus a les trajectòries curtes. Fins on sabem, aquesta és la primera vegada que aquestes tècniques són usades en aquest camp. Mostrem com aquesta aproximació millora les prestacions de qualsevol altra proposta a la classificació del model subjacent de difusió anòmala per a trajectòries curtes. Més enllà de la física hi ha les matemàtiques. Utilitzem la nostra arquitectura basada en xarxes recurrents neuronals per inferir els paràmetres que defineixen les trajectòries de Wu Baleanu. Mostrem que la nostra proposta pot inferir amb raonable precisió els paràmetres mu i nu. Sent la primera vegada, novament fins on arriba el nostre coneixement, que aquestes tècniques s'apliquen en aquest escenari. Estenem aquest treball a les equacions fraccionals discretes amb retard, obtenint resultats similars en termes de precisió. Addicionalment, mostrem que la mateixa arquitectura es pot fer servir per discriminar entre trajectòries amb i sense retard amb gran confiança. Finalment, també investiguem models fraccionals discrets. Hem analitzat esquemes de pas temporal amb la quadratura de Lubich en lloc del clàssic esquema d'ordre 1 d'Euler. Al primer estudi d'aquest nou paradigma hem comparat els diagrames de bifurcació dels mapes logístic i del sinus, obtinguts de la discretització d'Euler d'ordre 1, 2 i 1/2. / [EN] During the last decades the use of machine learning and artificial intelligence have showed an exponential growth in many areas of science. The fact that computer's hardware has increased its performance while lowering the price and the availability of open source frameworks have enabled the access to artificial intelligence to a broad range of researchers, hence democratizing the access to artificial intelligence methods to the research community. It is our belief that multi-disciplinarity is the key to new achievements, with teams composed of researchers with different backgrounds and fields of specialization. With this aim, we focused this thesis in using machine learning, artificial intelligence, deep learing, all of them being understood as part of a whole concept we concrete in artificial intelligence, to try to shed light to some problems from the fields of mathematics and physics. A deep learning architecture was developed and successfully benchmarked with the characterization of anomalous diffusion processes. Whereas traditional statistical methods had previously been used with this aim, deep learing methods, mainly based on recurrent neural networks have proved to outperform these clasical methods. Our architecture showed it can precisely infer the anomalous diffusion exponent and accurately classify trajectories among a given set of underlaying diffusion models. While recurrent neural networks irrupted in the recent years, convolutional network based models had been extensively tested in the field of image processing for more than 15 years. There exist many models and architectures, pre-trained and set to be used by the community. No further investigation needs to be done since the architecture have proved their value for years and are very well documented in the literature. Our goal was being able to used this well-known and reliable models with anomalous diffusion trajectories. We only needed to be able to convert a time series into an image, which we successfully did by applying gramian angular fields to the trajectories, focusing on short ones. To our knowledge this is the first time these techniques were used in this field. We show how this approach outperforms any other proposal in the underlaying diffusion model classification for short trajectories. Besides physics it is maths. We used our recurrent neural networks architecture to infer the parameters that define the Wu Baleanu trajectories. We show that our proposal can precisely infer both the mu and nu parameters with a reasonable confidence. Being the first time, to the best of our knowledge, that such techniques were applied to this scenario. We extend this work to the discrete delayed fractional equations, obtaining similar results in terms of precision. Additionally, we showed that the same architecture can be used to discriminate delayed from non-delayed trajectories with a high confidence. Finally, we also searched fractional discrete models. We have considered Lubich's quadrature time-stepping schemes instead of the classical Euler scheme of order 1. As the first study with this new paradigm, we compare the bifurcation diagrams for the logistic and sine maps obtained from Euler discretizations of orders 1, 2, and 1/2. / J.A.C. acknowledges support from ALBATROSS project (National Plan for Scientific and Technical Research and Innovation 2017-2020, No. PID2019-104978RB-I00). M.A.G.M. acknowledges funding from the Spanish Ministry of Education and Vocational Training (MEFP) through the Beatriz Galindo program 2018 (BEAGAL18/00203) and Spanish Ministry MINECO (FIDEUA PID2019- 106901GBI00/10.13039/501100011033). We thank M.A. Garc ́ıa-March for helpful comments and discussions on the topic. NF is sup- ported by the National University of Singapore through the Singapore International Graduate Student Award (SINGA) program. OGO and LS acknowledge funding from MINECO project, grant TIN2017-88476-C2-1-R. JAC acknowledges funding from grant PID2021-124618NB-C21 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”, by the “European Union”. We also thank funding for the open access charges from CRUE-Universitat Politècnica de València. / Garibo Orts, Ó. (2023). Anomalous Diffusion Characterization using Machine Learning Methods [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/192831 / Compendio