[PDF] Top 20 The quantum Monte Carlo method : application to problems in statistical physics

... the statistical fluctuations inherent in these calculations this agrees with the average value of Vref <vref> = 5730 ± 20 K/molecule obtained during the ...the quantum Monte Carlo ... See full document

... Quantum physics presents to us consistent and accurate pictures of systems and it is nowa- days seen more reliable than the classical physics in investigating the properties of ...small-scale ... See full document

... (i) quantum problems are very computationally intensive; (ii) the inherently parallel nature of Monte Carlo applications makes efficient use of Grid ...(distributed) Monte Carlo ... See full document

... I entered North Carolina State University as a major in physics. I worked one semester for the WebAssign project, and immediately sought out a place to work in a ‘real’ lab. I approached Dr. Jacqueline Krim for a ... See full document

... the statistical problem. In addition, the results of statistical analysis of water level monitoring data, water column permeability and displacement in 2 directions (vertical and horizontal) in the 5 above ... See full document

... The most atomic theories arise from the independent electron shell model which assumes that every electron is moving in a field combined of the nucleus and the mean distribution of the other electrons. In this model, the ... See full document

... The Monte Carlo method is intensively used in various areas of scientific ...computational physics to fluid dynamics, it has seen exponential growth with the introduction of computationally ... See full document

... Quantum Monte Carlo (QMC) methods have proved to be very useful for studies of many-body quantum ...structure problems it is often necessary to eliminate the atomic cores using ... See full document

... Monte Carlo analysis has played an important role for many years in the investigation of statistical estimators whose properties cannot be adequately determined through mathematical techniques ... See full document

... the Monte Carlo method refers to a technique through which random numbers are used to solve ...the method emerged in a game of solitaire in which Stan Ulam considered the use of successive ... See full document

... energy Monte Carlo but often slower than diffusion solutions, and solution accuracy, better describing angular flux effects than diffusion but maintaining errors of energy, space, and angle discretization’s ... See full document

... The main motivation behind this paper is to investigate the applicability and efficiency of a conceptually simple MC method for solving machining optimization problems. Although the aforementioned ... See full document

... particle physics since their discovery half a century ...their physics impact, make decisions about detector design and optimization, analyze the collected data samples, and evaluate systematic ... See full document

... atmospheric physics (HEAP) includes terrestrial gamma-ray flashes, electron–positron beams and gamma-ray glows from thun- ...the Monte Carlo codes Geant4, EGS5 and FLUKA developed in other fields of ... See full document

... A Monte Carlo simulation of the nuclear reaction process will be able to provide fully correlated information, ...the statistical Hauser- Feshbach theory [1] by means of a stochastic method, ... See full document

... released-node method quite challenging to apply since it requires correcting the nodal surface change by sampling low-density regions with walkers travelling large ... See full document

... the quantum-mechanical de- scription of ...Its quantum analog was discussed by Feynman 关12兴 and Feynman and Cohen 关13兴 in the contexts of excitations in 4 He and the effective mass of a 3 He impurity in ... See full document

... Typically, the posterior distribution is intractable, in the sense that direct sampling is unavail- able. One way to circumvent this problem is to use a Markov chain Monte Carlo (MCMC) approach to sample ... See full document

... using Monte Carlo for allocating redundancy among subsystems so as to achieve maximum reliability of a complex systems subjected to multiple constraints which may be linear, non linear separable or non ... See full document

... The Monte Carlo localization method is based on the Bayes filtering the- ory, and the main idea is by utilizing the new observa- tion from the adjacent anchor nodes within the range, the sample and ... See full document