Search results for "algorithm"
showing 10 items of 4887 documents
Speeding up of microstructure reconstruction: II. Application to patterns of poly-dispersed islands
2015
We report a fast, efficient and credible statistical reconstruction of any two-phase patterns of islands of miscellaneous shapes and poly-dispersed in sizes. In the proposed multi-scale approach called a weighted doubly-hybrid, two different pairs of hybrid descriptors are used. As the first pair, we employ entropic quantifiers, while correlation functions are the second pair. Their competition allows considering a wider spectrum of morphological features. Instead of a standard random initial configuration, a synthetic one with the same number of islands as that of the target is created by a cellular automaton. This is the key point for speeding-up of microstructure reconstruction, making u…
Reconstruction of an effective magnon mean free path distribution from spin Seebeck measurements in thin films
2017
A thorough understanding of the mean-free-path (MFP) distribution of the energy carriers is crucial to engineer and tune the transport properties of materials. In this context, a significant body of work has investigated the phonon and electron MFP distribution, however, similar studies of the magnon MFP distribution have not been carried out so far. In this work, we used thickness-dependence measurements of the longitudinal spin Seebeck (LSSE) effect of yttrium iron garnet films to reconstruct the cumulative distribution of a SSE related effective magnon MFP. By using the experimental data reported by Guo et al. [Phys. Rev. X 6, 031012 (2016)], we adapted the phonon MFP reconstruction algo…
Speeding up of microstructure reconstruction: I. Application to labyrinth patterns
2011
Recently, entropic descriptors based the Monte Carlo hybrid reconstruction of the microstructure of a binary/greyscale pattern has been proposed (Piasecki 2011 Proc. R. Soc. A 467 806). We try to speed up this method applied in this instance to the reconstruction of a binary labyrinth target. Instead of a random configuration, we propose to start with a suitable synthetic pattern created by cellular automaton. The occurrence of the characteristic attributes of the target is the key factor for reducing the computational cost that can be measured by the total number of MC steps required. For the same set of basic parameters, we investigated the following simulation scenarios: the biased/rando…
Parallelization strategies for density matrix renormalization group algorithms on shared-memory systems
2003
Shared-memory parallelization (SMP) strategies for density matrix renormalization group (DMRG) algorithms enable the treatment of complex systems in solid state physics. We present two different approaches by which parallelization of the standard DMRG algorithm can be accomplished in an efficient way. The methods are illustrated with DMRG calculations of the two-dimensional Hubbard model and the one-dimensional Holstein-Hubbard model on contemporary SMP architectures. The parallelized code shows good scalability up to at least eight processors and allows us to solve problems which exceed the capability of sequential DMRG calculations.
Quasi-continuous-time impurity solver for the dynamical mean-field theory with linear scaling in the inverse temperature
2013
We present an algorithm for solving the self-consistency equations of the dynamical mean-field theory (DMFT) with high precision and efficiency at low temperatures. In each DMFT iteration, the impurity problem is mapped to an auxiliary Hamiltonian, for which the Green function is computed by combining determinantal quantum Monte Carlo (BSS-QMC) calculations with a multigrid extrapolation procedure. The method is numerically exact, i.e., yields results which are free of significant Trotter errors, but retains the BSS advantage, compared to direct QMC impurity solvers, of linear (instead of cubic) scaling with the inverse temperature. The new algorithm is applied to the half-filled Hubbard mo…
Parallelization of a Lattice Boltzmann Suspension Flow Solver
2002
We have applied a parallel Lattice Boltzmann method to solve the behaviour of the suspension flow. The complex behaviour of the suspension flow cannot be solved by analytical methods, so simulations are the only way to study it. Usually the size of an interesting problem is so big that calculation time on one processor is too long, and this can be solved by parallel program. We have written a parallel suspension flow solver and tested it on massive parallel computers. The measured performance of our program show that the parallelization of suspension particles was successful. We also show that over one million particles can be simulated.
SPATIAL MULTIFRACTALITY OF ELECTRONIC STATES AND THE METAL-INSULATOR TRANSITION IN DISORDERED SYSTEMS
1993
For the investigation of the spatial behavior of electronic wave functions in disordered systems, we employ the Anderson model of localization. The eigenstates of the corresponding Hamiltonian are calculated numerically by means of the Lanczos algorithm and are analyzed with respect to their spatial multifractal properties. We find that the wave functions show spatial multifractality for all parameter cases not too far away from the metal-insulator transition (MIT) which separates localized from extended states in this model. Exactly at the MIT, multifractality is expected to exist on all length scales larger than the lattice spacing. It is found that the corresponding singularity spectrum…
Conditional Entropy-Based Evaluation of Information Dynamics in Physiological Systems
2014
We present a framework for quantifying the dynamics of information in coupled physiological systems based on the notion of conditional entropy (CondEn). First, we revisit some basic concepts of information dynamics, providing definitions of self entropy (SE), cross entropy (CE) and transfer entropy (TE) as measures of information storage and transfer in bivariate systems. We discuss also the generalization to multivariate systems, showing the importance of SE, CE and TE as relevant factors in the decomposition of the system predictive information. Then, we show how all these measures can be expressed in terms of CondEn, and devise accordingly a framework for their data-efficient estimation.…
Multiscale Information Storage of Linear Long-Range Correlated Stochastic Processes
2019
Information storage, reflecting the capability of a dynamical system to keep predictable information during its evolution over time, is a key element of intrinsic distributed computation, useful for the description of the dynamical complexity of several physical and biological processes. Here we introduce a parametric approach which allows one to compute information storage across multiple timescales in stochastic processes displaying both short-term dynamics and long-range correlations (LRC). Our analysis is performed in the popular framework of multiscale entropy, whereby a time series is first "coarse grained" at the chosen timescale through low-pass filtering and downsampling, and then …
Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range co…
2017
Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse fields. However, the practical application of entropy methods is challenging, due to the variety of entropy measures and estimators and the complexity of real-world time series, including nonstationarities and long-range correlations (LRC). We conduct a systematic study on the performance, bias, and limitations of three basic measures (entropy, conditional entropy, information storage) and three traditionally used estimators (linear, kernel, nearest neighbor). We investigate the dependence of entropy measures on estimator- and process-specific parameters, and we show the effects of three types of …