8 resultados para Monte-Carlo approach

em Universidade Federal do Rio Grande do Norte(UFRN)


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Conselho Nacional de Desenvolvimento Científico e Tecnológico

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We studied the Ising model ferromagnetic as spin-1/2 and the Blume-Capel model as spin-1, > 0 on small world network, using computer simulation through the Metropolis algorithm. We calculated macroscopic quantities of the system, such as internal energy, magnetization, specific heat, magnetic susceptibility and Binder cumulant. We found for the Ising model the same result obtained by Koreans H. Hong, Beom Jun Kim and M. Y. Choi [6] and critical behavior similar Blume-Capel model

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The Monte Carlo method is accurate and is relatively simple to implement for the solution of problems involving complex geometries and anisotropic scattering of radiation as compared with other numerical techniques. In addition, differently of what happens for most of numerical techniques, for which the associated simulations computational time tends to increase exponentially with the complexity of the problems, in the Monte Carlo the increase of the computational time tends to be linear. Nevertheless, the Monte Carlo solution is highly computer time consuming for most of the interest problems. The Multispectral Energy Bundle model allows the reduction of the computational time associated to the Monte Carlo solution. The referred model is here analyzed for applications in media constituted for nonparticipating species and water vapor, which is an important emitting species formed during the combustion of hydrocarbon fuels. Aspects related to computer time optimization are investigated the model solutions are compared with benchmark line-by-line solutions

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In this thesis we deal with a class of composed networks that are formed by two tree networks, TP and TA, whose end points touches each other through a bipartite network BPA. We explore this network using a functional approach. We are interested in what extend the topology, or the structure, of TX (X = A or P) determines the links of BPA. This composed structure is an useful model in evolutionary biology, where TP and TA are the phylogenetic trees of plants and animals that interact in an ecological community. We use in this thesis two cases of mutualist interactions: frugivory and pollinator networks. We analyse how the phylogeny of TX determines or is correlated with BPA using a Monte Carlo approach. We use the phylogenetic distance among elements that interact with a given species to construct an index κ that quantifies the influence of TX over BPA. The algorithm is based in the assumption that interaction matrices that follows a phylogeny of TX have a total phylogenetic distance smaller than the average distance of an ensemble of Monte Carlo realizations generated by an adequate shuffling data. We find that the phylogeny of animals species has an effect on the ecological matrix that is more marked than plant phylogeny

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High-precision calculations of the correlation functions and order parameters were performed in order to investigate the critical properties of several two-dimensional ferro- magnetic systems: (i) the q-state Potts model; (ii) the Ashkin-Teller isotropic model; (iii) the spin-1 Ising model. We deduced exact relations connecting specific damages (the difference between two microscopic configurations of a model) and the above mentioned thermodynamic quanti- ties which permit its numerical calculation, by computer simulation and using any ergodic dynamics. The results obtained (critical temperature and exponents) reproduced all the known values, with an agreement up to several significant figures; of particular relevance were the estimates along the Baxter critical line (Ashkin-Teller model) where the exponents have a continuous variation. We also showed that this approach is less sensitive to the finite-size effects than the standard Monte-Carlo method. This analysis shows that the present approach produces equal or more accurate results, as compared to the usual Monte Carlo simulation, and can be useful to investigate these models in circumstances for which their behavior is not yet fully understood

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We study the critical behavior of the one-dimensional pair contact process (PCP), using the Monte Carlo method for several lattice sizes and three different updating: random, sequential and parallel. We also added a small modification to the model, called Monte Carlo com Ressucitamento" (MCR), which consists of resuscitating one particle when the order parameter goes to zero. This was done because it is difficult to accurately determine the critical point of the model, since the order parameter(particle pair density) rapidly goes to zero using the traditional approach. With the MCR, the order parameter becomes null in a softer way, allowing us to use finite-size scaling to determine the critical point and the critical exponents β, ν and z. Our results are consistent with the ones already found in literature for this model, showing that not only the process of resuscitating one particle does not change the critical behavior of the system, it also makes it easier to determine the critical point and critical exponents of the model. This extension to the Monte Carlo method has already been used in other contact process models, leading us to believe its usefulness to study several others non-equilibrium models

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Stellar differential rotation is an important key to understand hydromagnetic stellar dynamos, instabilities, and transport processes in stellar interiors as well as for a better treatment of tides in close binary and star-planet systems. The space-borne high-precision photometry with MOST, CoRoT, and Kepler has provided large and homogeneous datasets. This allows, for the first time, the study of differential rotation statistically robust samples covering almost all stages of stellar evolution. In this sense, we introduce a method to measure a lower limit to the amplitude of surface differential rotation from high-precision evenly sampled photometric time series such as those obtained by space-borne telescopes. It is designed for application to main-sequence late-type stars whose optical flux modulation is dominated by starspots. An autocorrelation of the time series is used to select stars that allow an accurate determination of spot rotation periods. A simple two-spot model is applied together with a Bayesian Information Criterion to preliminarily select intervals of the time series showing evidence of differential rotation with starspots of almost constant area. Finally, the significance of the differential rotation detection and a measurement of its amplitude and uncertainty are obtained by an a posteriori Bayesian analysis based on a Monte Carlo Markov Chain (hereafter MCMC) approach. We apply our method to the Sun and eight other stars for which previous spot modelling has been performed to compare our results with previous ones. The selected stars are of spectral type F, G and K. Among the main results of this work, We find that autocorrelation is a simple method for selecting stars with a coherent rotational signal that is a prerequisite to a successful measurement of differential rotation through spot modelling. For a proper MCMC analysis, it is necessary to take into account the strong correlations among different parameters that exists in spot modelling. For the planethosting star Kepler-30, we derive a lower limit to the relative amplitude of the differential rotation. We confirm that the Sun as a star in the optical passband is not suitable for a measurement of the differential rotation owing to the rapid evolution of its photospheric active regions. In general, our method performs well in comparison with more sophisticated procedures used until now in the study of stellar differential rotation

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This work presents a new model for the Heterogeneous p-median Problem (HPM), proposed to recover the hidden category structures present in the data provided by a sorting task procedure, a popular approach to understand heterogeneous individual’s perception of products and brands. This new model is named as the Penalty-free Heterogeneous p-median Problem (PFHPM), a single-objective version of the original problem, the HPM. The main parameter in the HPM is also eliminated, the penalty factor. It is responsible for the weighting of the objective function terms. The adjusting of this parameter controls the way that the model recovers the hidden category structures present in data, and depends on a broad knowledge of the problem. Additionally, two complementary formulations for the PFHPM are shown, both mixed integer linear programming problems. From these additional formulations lower-bounds were obtained for the PFHPM. These values were used to validate a specialized Variable Neighborhood Search (VNS) algorithm, proposed to solve the PFHPM. This algorithm provided good quality solutions for the PFHPM, solving artificial generated instances from a Monte Carlo Simulation and real data instances, even with limited computational resources. Statistical analyses presented in this work suggest that the new algorithm and model, the PFHPM, can recover more accurately the original category structures related to heterogeneous individual’s perceptions than the original model and algorithm, the HPM. Finally, an illustrative application of the PFHPM is presented, as well as some insights about some new possibilities for it, extending the new model to fuzzy environments