A Markov-property Monte Carlo method: One-dimensional Ising model

In this paper we introduce a new Monte Carlo procedure based on the Markov property. This procedure is particularly well suited to massively parallel computation. We illustrate the method on the critical phenomena of the well known one-dimensional Ising model. In the course of this work we found that the autocorrelation time for the Metropolis Monte Carlo algorithm is closely given by the square of the correlation length. We find speedup factors of the order of 1 million for the method as implemented on the CM2 relative to a serial machine. Our procedure gives error estimates which are quite consistent with the observed deviations from the analytically known exact results.     

Nonlinear noise reduction through Monte Carlo sampling

We consider the problem of nonlinear noise reduction within the framework of Bayesian Theory. This enables us to place appropriate weights on the measurement and dynamic errors and thereby avoid over cleaning the data. Using a Metropolis-Hastings sampler, we are able to achieve robust noise reduction without the introduction of ad hoc parameters but at the expense of higher computational complexity. Such an algorithm should also allow us to explore the potential and limitations of other noise reduction methods. (c) 1998 American Institute of Physics.     

A new Monte Carlo power method for the eigenvalue problem of transfer matrices

We propose a new Monte Carlo method for calculating eigenvalues of transfer matrices leading to free energies and to correlation lengths of classical and quantum many-body systems. Generally, this method can be applied to the calculation of the maximum eigenvalue of a nonnegative matrix  such that all the matrix elements of Â^k are strictly positive for an integerk. This method is based on a new representation of the maximum eigenvalue of the matrix  as the thermal average of a certain observable of a many-body system. Therefore one can easily calculate the maximum eigenvalue of a transfer matrix leading to the free energy in the standard Monte Carlo simulations, such as the Metropolis algorithm. As test cases, we calculate the free energies of the square-lattice Ising model and of the spin-1/2XY Heisenberg chain. We also prove two useful theorems on the ergodicity in quantum Monte Carlo algorithms, or more generally, on the ergodicity of Monte Carlo algorithms using our new representation of the maximum eigenvalue of the matrixÂ.     

A Monte Carlo method for simulating fractal surfaces

A Monte Carlo method is presented for simulating rough surfaces with the fractal behavior. The simulation is based on power-law size distribution of asperity diameter and self-affine property of roughness on surfaces. A probability model based on random number for asperity sizes is developed to generate the surfaces. By iteration, this method can be used to simulate surfaces that exhibit the aforementioned properties. The results indicate that the variation of the surface topography is related to the effects of scaling constant G and the fractal dimension D of the profile of rough surface. The larger value of D or smaller value of G signifies the smoother surface topography. This method may have the potential in prediction of the transport properties (such as friction, wear, lubrication, permeability and thermal or electrical conductivity, etc.) on rough surfaces.     

A quantum Monte Carlo method for nucleon systems

We describe a quantum Monte Carlo method for Hamiltonians which include tensor and other spin interactions such as those that are commonly encountered in nuclear structure calculations. The main ingredients are a Hubbard-Stratonovich transformation to uncouple the spin degrees of freedom along with a fixed node approximation to maintain stability. We apply the method to neutron matter interacting with a central, spin-exchange, and tensor forces. The addition of isospin degrees of freedom is straightforward.     

Projector Quantum Monte Carlo without minus-sign problem

Quantum Monte Carlo techniques often suffer from the so-called minus-sign problem. This paper explores a possibility to circumvent this fundamental problem by combining the Projector Quantum Monte Carlo method with the variational principle. Results are presented for the two-dimensional Hubbard model.     

模拟回火马尔可夫链蒙特卡罗全波形分析方法

针对传统的全波形分析方法不能快速自动处理全波形数据的缺点,提出了一种模拟回火马尔可夫链蒙特卡罗全波形分析法,用于求解全波形数据中的波峰数和峰值位置等参量.该方法采用Metropolis更新策略求解波峰数量和噪声两个参量,以达到快速求解的目的;而峰值位置和波峰幅值则采用改进的模拟回火策略求解,通过添加的主动干预回火步骤实现对参量更新过程的有效探测,以满足对速度或运算收敛性的要求.模拟回火马尔可夫链蒙特卡罗全波形分析方法以马尔可夫算法为基础,仍保持马氏链的收敛性,从而保证本方法具有良好的鲁棒性,实现对全波形数据的自动化处理.     

Estimation of the four-wave mixing noise probability-density function by the multicanonical Monte Carlo method

The performance of high-powered wavelength-division multiplexed (WDM) optical networks can be severely degraded by four-wave-mixing- (FWM-) induced distortion. The multicanonical Monte Carlo method (MCMC) is used to calculate the probability-density function (PDF) of the decision variable of a receiver, limited by FWM noise. Compared with the conventional Monte Carlo method previously used to estimate this PDF, the MCMC method is much faster and can accurately estimate smaller error probabilities. The method takes into account the correlation between the components of the FWM noise, unlike the Gaussian model, which is shown not to provide accurate results.     

First-passage Monte Carlo algorithm: diffusion without all the hops

We present a novel Monte Carlo algorithm for N diffusing finite particles that react on collisions. Using the theory of first-passage processes and time dependent Green's functions, we break the difficult N-body problem into independent single- and two-body propagations circumventing numerous diffusion hops used in standard Monte Carlo simulations. The new algorithm is exact, extremely efficient, and applicable to many important physical situations in arbitrary integer dimensions.     

A new random-number generator for multispin Monte Carlo algorithms

We develop a novel multispin coded random number generator algorithm to compute bits equal to 1 with probabilityp. Compared to previously used algorithms, this generator is at least equally fast and allows for an arbitrary accuracy of the computed probability without any significant increase in time. An explicit implementation of the algorithm is given for a Cray-1 vector computer, and the modifications for other machines are discussed. Finally, the algorithm is tested by computing the magnetization of the two-dimensional Ising model. The measured speed of the program is 57 million spin-flips per second. The agreement with theoretical values is found to remain very satisfying even when quite close (-0.5%) to the critical temperature.     

A Monte Carlo method for estimating the correlation exponent

We propose a Monte Carlo method for estimating the correlation exponent of a stationary ergodic sequence. The estimator can be considered as a bootstrap version of the classical Hill estimator. A simulation study shows that the method yields reasonable estimates.     

A differentially weighted Monte Carlo method for two-component coagulation

The direct simulation Monte Carlo (DSMC) method for population balance modeling is capable of retaining the history of each simulation particle and is thus able to deal with multivariate properties in a simple and straightforward manner. As opposed to conventional DSMC approaches that track equally weighted simulation particles, a differentially weighted Monte Carlo method is extended to simulate two-component coagulation processes and is thereby able to simulate the micromixing of the components. A new feature of the method for this bivariate population balance modeling is that it is possible to specify how the simulation particles are distributed over the compositional axis. This allows us to obtain information about particles in those regions of the size and composition distribution functions where the non-weighted MC methods place insufficient simulation particles to obtain an inaccurate solution. The new feature results in lower statistical noise for simulating two-component coagulation, which is validated by using two-component coagulation cases for which analytical solutions exist (a discrete process with sum kernel for initial monodisperse populations and a process with constant kernel for initial polydisperse populations).     

Monte Carlo method and sensitivity estimations

It is shown that, starting from any existing Monte Carlo algorithm for estimation of a physical quantity A, it is possible to implement a simple additional procedure that simultaneously estimates the sensitivity of A to any problem parameter. The corresponding supplementary cost is very low as no additional random sampling is required. The principle is presented on a formal basis and simple radiative transfer examples are used for illustration.     

Improved Monte Carlo renormalization group method

An extensive program to analyze critical systems using an improved Monte Carlo renormalization group method (IMCRG),⁽¹⁾ being undertaken at LANL and Cornell, is described. Here we first briefly rview the method and then list some of the topics being investigated.     

Monte Carlo simulation of excess noise in heterojunction avalanche photodetector

A Monte Carlo (MC) simulation of excess noise in heterojunction avalanche photodetector (APD), made up of InP/InGaAs, is made. The simulation is based on the hard threshold dead space consideration in the displaced exponential model of the distribution of ionization path lengths. Impact ionization and multiplication of electrons as function of ionizing electric field are also studied. The multiplication and noise are seen to be reduced compared to those in component materials. The simulated results are seen to agree well with the reports of other theoretical predictions and experimental results published in literatures.