160 resultados para dynamical dimension
em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"
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This is the first paper in a two-part series devoted to studying the Hausdorff dimension of invariant sets of non-uniformly hyperbolic, non-conformal maps. Here we consider a general abstract model, that we call piecewise smooth maps with holes. We show that the Hausdorff dimension of the repeller is strictly less than the dimension of the ambient manifold. Our approach also provides information on escape rates and dynamical dimension of the repeller.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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The spectral principle of Connes and Chamseddine is used as a starting point to define a discrete model for Euclidean quantum gravity. Instead of summing over ordinary geometries, we consider the sum over generalized geometries where topology, metric, and dimension can fluctuate. The model describes the geometry of spaces with a countable number n of points, and is related to the Gaussian unitary ensemble of Hermitian matrices. We show that this simple model has two phases. The expectation value
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Predictability is related to the uncertainty in the outcome of future events during the evolution of the state of a system. The cluster weighted modeling (CWM) is interpreted as a tool to detect such an uncertainty and used it in spatially distributed systems. As such, the simple prediction algorithm in conjunction with the CWM forms a powerful set of methods to relate predictability and dimension.
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Forecasting, for obvious reasons, often become the most important goal to be achieved. For spatially extended systems (e.g. atmospheric system) where the local nonlinearities lead to the most unpredictable chaotic evolution, it is highly desirable to have a simple diagnostic tool to identify regions of predictable behaviour. In this paper, we discuss the use of the bred vector (BV) dimension, a recently introduced statistics, to identify the regimes where a finite time forecast is feasible. Using the tools from dynamical systems theory and Bayesian modelling, we show the finite time predictability in two-dimensional coupled map lattices in the regions of low BV dimension. © Indian Academy of Sciences.
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We study non-hyperbolic repellers of diffeomorphisms derived from transitive Anosov diffeomorphisms with unstable dimension 2 through a Hopf bifurcation. Using some recent abstract results about non-uniformly expanding maps with holes, by ourselves and by Dysman, we show that the Hausdorff dimension and the limit capacity (box dimension) of the repeller are strictly less than the dimension of the ambient manifold.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Bloch and Wannier functions of the Kohn type for a quite general one-dimensional Hamiltonian with inversion symmetry are studied. Important clarifications on null minigaps and the symmetry of those functions are given, with emphasis on the Kronig-Penney model. The lack of a general selection rule on the miniband index for optical transitions between edge states in semiconductor superlattices is discussed. A direct method for the calculation of Wannier-Kohn functions is presented.
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The systems of water distribution from groundwater wells can be monitored using the changes observed on its dynamical behavior. In this paper, artificial neural networks are used to estimate the depth of the dynamical water level of groundwater wells in relation to water flow, operation time and rest time. Simulation results are presented to demonstrate the validity of the proposed approach. These results have shown that artificial neural networks can be effectively used for the identification and estimation of parameters related to systems of water distribution.
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The accurate identification of features of dynamical grounding systems are extremely important to define the operational safety and proper functioning of electric power systems. Several experimental tests and theoretical investigations have been carried out to obtain characteristics and parameters associated with the technique of grounding. The grounding system involves a lot of non-linear parameters. This paper describes a novel approach for mapping characteristics of dynamical grounding systems using artificial neural networks. The network acts as identifier of structural features of the grounding processes. So that output parameters can be estimated and generalized from an input parameter set. The results obtained by the network are compared with other approaches also used to model grounding systems.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)