Search results for "Statistical physics"
showing 10 items of 1402 documents
On the first- and second-order statistics of the capacity of N*Nakagami-m channels for applications in cooperative networks
2012
This article deals with the derivation and analysis of the statistical properties of the instantaneous channel capacitya of N*Nakagami-m channels, which has been recently introduced as a suitable stochastic model for multihop fading channels. We have derived exact analytical expressions for the probability density function (PDF), cumulative distribution function (CDF), level-crossing rate (LCR), and average duration of fades (ADF) of the instantaneous channel capacity of N*Nakagami-m channels. For large number of hops, we have studied the first-order statistics of the instantaneous channel capacity by assuming that the fading amplitude of the channel can approximately be modeled as a lognor…
Using Applications and Tools to Visualize ab initio Calculations Performed in VASP
2018
Visualization of the results of the ab initio calculations is important for the analysis of these results. It improves the quality of the analysis by supplementing the plain numbers received as the output of the calculations with various graphical images and facilitates the analysis of the results. In addition to that visualization helps avoiding some mistakes or inconsistencies. Various tools have been used in this work to construct the unit cell models of the calculated lattices, to check and analyze the calculated lattice structure before and after the relaxation, to plot total and difference electron charge density maps.
Revisitation of Nonorthogonal Spin Adaptation in Coupled Cluster Theory.
2015
The benefits of what is alternatively called a nonorthogonally spin-adapted, spin-free, or orbital representation of the coupled cluster equations is discussed relative to orthogonally spin-adapted, spin-orbital, and spin-integrated theories. In particular, specific linear combinations of the orbital cluster amplitudes, denoted spin-summed amplitudes, are shown to reduce the number of contractions that must be explicitly performed and to simplify the expressions and their derivation. The computational efficiency of the spin-summed approach is discussed and compared to orthogonally spin-adapted and spin-integrated approaches. The spin-summed approach is shown to have significant computationa…
Modelling and Analysis of Non-Stationary Multipath Fading Channels with Time-Variant Angles of Arrival
2017
In mobile radio channel modelling, it is generally assumed that the angles of arrival (AOAs) are independent of time. This assumption does in general not agree with real-world channels in which the AOAs vary with the position of a moving receiver. In this paper, we first present a mathematical model for the time-variant AOAs. This model serves as the basis for the development of two non-stationary multipath fading channels models. The statistical properties of both channel models are analysed with emphasis on the time-dependent autocorrelation function (ACF), time-dependent mean Doppler shift, time-dependent Doppler spread, and the Wigner-Ville spectrum. It is shown that these characteristi…
Non-equilibrium Markov state modeling of periodically driven biomolecules
2019
Molecular dynamics simulations allow to study the structure and dynamics of single biomolecules in microscopic detail. However, many processes occur on time scales beyond the reach of fully atomistic simulations and require coarse-grained multiscale models. While systematic approaches to construct such models have become available, these typically rely on microscopic dynamics that obey detailed balance. In vivo, however, biomolecules are constantly driven away from equilibrium in order to perform specific functions and thus break detailed balance. Here we introduce a method to construct Markov state models for systems that are driven through periodically changing one (or several) external p…
Molecular dynamics simulations in hybrid particle-continuum schemes: Pitfalls and caveats
2017
Heterogeneous multiscale methods (HMM) combine molecular accuracy of particle-based simulations with the computational efficiency of continuum descriptions to model flow in soft matter liquids. In these schemes, molecular simulations typically pose a computational bottleneck, which we investigate in detail in this study. We find that it is preferable to simulate many small systems as opposed to a few large systems, and that a choice of a simple isokinetic thermostat is typically sufficient while thermostats such as Lowe-Andersen allow for simulations at elevated viscosity. We discuss suitable choices for time steps and finite-size effects which arise in the limit of very small simulation bo…
Spreading of Competing Information in a Network
2020
We propose a simple approach to investigate the spreading of news in a network. In more detail, we consider two different versions of a single type of information, one of which is close to the essence of the information (and we call it good news), and another of which is somehow modified from some biased agent of the system (fake news, in our language). Good and fake news move around some agents, getting the original information and returning their own version of it to other agents of the network. Our main interest is to deduce the dynamics for such spreading, and to analyze if and under which conditions good news wins against fake news. The methodology is based on the use of ladder fermion…
CLUSTER MONTE CARLO ALGORITHMS IN STATISTICAL MECHANICS
1992
The cluster Monte Carlo method, where variables are updated in groups, is very efficient at second order phase transitions. Much better results can be obtained with less computer time. This article reviews the method of Swendsen and Wang and some of its applications.
Group Metropolis Sampling
2017
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…
Rejection-Free Monte Carlo
2019
So far, we have been using the rejection Monte Carlo algorithms. To remind us, the algorithms proceed from state x to possible state \(x'\) as outlined in Algorithm 1.