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A Monte Carlo method is presented to evaluate quantum states with many particles moving in the continuum. The scattering state is generated at each time by a Monte Carlo random sampling algorithm. The same calculation are repeated until the…

Nuclear Theory · Physics 2013-06-06 Zhen-Xiang Xu , Chong Qi

In the framework of uncertainty quantification, we consider a quantity of interest which depends non-smoothly on the high-dimensional parameter representing the uncertainty. We show that, in this situation, the multilevel Monte Carlo…

Numerical Analysis · Mathematics 2017-06-27 Laura Scarabosio

We present a Monte Carlo study of the bond and site directed (oriented) percolation models in $(d+1)$ dimensions on simple-cubic and body-centered-cubic lattices, with $2 \leq d \leq 7$. A dimensionless ratio is defined, and an analysis of…

Statistical Mechanics · Physics 2013-10-11 Junfeng Wang , Zongzheng Zhou , Qingquan Liu , Timothy M. Garoni , Youjin Deng

We analyze a new Monte Carlo method which uses transition matrix in the space of energy. This method gives an efficient reweighting technique. The associated artificial dynamics is a constrained random walk in energy, producing the result…

Statistical Mechanics · Physics 2009-10-31 Jian-Sheng Wang

Most networks of interest do not live in isolation. Instead they form components of larger systems in which multiple networks with distinct topologies coexist and where elements distributed amongst different networks may interact directly.…

Disordered Systems and Neural Networks · Physics 2009-07-07 E. A. Leicht , Raissa M. D'Souza

By bootstrap percolation we mean the following deterministic process on a graph $G$. Given a set $A$ of vertices "infected" at time 0, new vertices are subsequently infected, at each time step, if they have at least $r\in\mathbb{N}$…

Combinatorics · Mathematics 2009-08-31 József Balogh , Béla Bollobás , Robert Morris

The use of degree-degree correlations to model realistic networks which are characterized by their Pearson's coefficient, has become widespread. However the effect on how different correlation algorithms produce different results on…

Physics and Society · Physics 2011-12-30 L. D. Valdez , C. Buono , L. A. Braunstein , P. A. Macri

In this Ph.D. thesis quantum Monte Carlo methods are applied to investigate the properties of a number of ultracold quantum systems. In Chapter 1 we discuss the analytical approaches and approximations used in the subsequent Chapters; also…

Quantum Gases · Physics 2013-01-10 Oleg N. Osychenko

The use of computers in statistical physics is common because the sheer number of equations that describe the behavior of an entire system particle by particle often makes it impossible to solve them exactly. Monte Carlo methods form a…

Physics Education · Physics 2017-04-27 Matjaz Perc

Closed-form stochastic filtering equations can be derived in a general setting where probability distributions are replaced by some specific outer measures. In this article, we study how the principles of the sequential Monte Carlo method…

Methodology · Statistics 2018-05-07 Jeremie Houssineau , Branko Ristic

The properties of the similarity transformation in percolation theory in the complex plane of the percolation probability are studied. It is shown that the percolation problem on a two-dimensional square lattice reduces to the Mandelbrot…

Disordered Systems and Neural Networks · Physics 2008-02-03 M. V. Entin , G. M. Entin

Quantum mechanics for many-body systems may be reduced to the evaluation of integrals in 3N dimensions using Monte-Carlo, providing the Quantum Monte Carlo ab initio methods. Here we limit ourselves to expectation values for trial…

Computational Physics · Physics 2010-11-22 John Robert Trail , Ryo Maezono

Employing a classical density-functional description of liquid environments, we introduce a rigorous method for the diffusion quantum Monte Carlo calculation of free energies and thermodynamic averages of solvated systems that requires…

The aim of this paper is to introduce a new Monte Carlo method based on importance sampling techniques for the simulation of stochastic differential equations. The main idea is to combine random walk on squares or rectangles methods with…

Probability · Mathematics 2010-10-22 Madalina Deaconu , Antoine Lejay

Percolation establishes the connectivity of complex networks and is one of the most fundamental critical phenomena for the study of complex systems. On simple networks, percolation displays a second-order phase transition; on multiplex…

Adaptation and Self-Organizing Systems · Physics 2023-03-14 Hanlin Sun , Filippo Radicchi , Jürgen Kurths , Ginestra Bianconi

A percolation model is proposed to explain the structural phase transitions found in Li$_{1-x}$H$_x$IO$_3$ mixed crystals as a function of the concentration parameter $x$. The percolation thresholds are obtained from Monte Carlo simulations…

Statistical Mechanics · Physics 2009-11-10 P. H. L. Martins , J. A. Plascak , M. A. Pimenta

We study site percolation on uniform quadrangulations of the upper half plane. The main contribution is a method for applying Angel's peeling process, in particular for analyzing an evolving boundary condition during the peeling. Our method…

Probability · Mathematics 2019-12-16 Jakob E. Björnberg , Sigurdur Örn Stefánsson

The tolerancing step has a great importance in the design process. It characterises the relationship between the different sectors of the product life cycle: Design, Manufacturing and Control. We can distinguish several methods to assist…

Other Computer Science · Computer Science 2011-02-16 Abdessalem Hassani , Nizar Aifaoui , Abdelmajid Benamara , Serge Samper

This topical review describes the methodology of continuum variational and diffusion quantum Monte Carlo calculations. These stochastic methods are based on many-body wave functions and are capable of achieving very high accuracy. The…

Materials Science · Physics 2010-02-11 R. J. Needs , M. D. Towler , N. D. Drummond , P. Lopez Rios

This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the problem of computing the gradient of an expectation of a…

Machine Learning · Statistics 2020-09-30 Shakir Mohamed , Mihaela Rosca , Michael Figurnov , Andriy Mnih