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Stochastic sampling algorithms such as Langevin Monte Carlo are inspired by physical systems in a heat bath. Their equilibrium distribution is the canonical ensemble given by a prescribed target distribution, so they must balance…

High Energy Physics - Lattice · Physics 2025-05-06 Jakob Robnik , Uroš Seljak

We propose a new Monte Carlo technique in which the degeneracy of energy states is obtained with a Markovian process analogous to that of Metropolis used currently in canonical simulations. The obtained histograms are much broader than…

Statistical Mechanics · Physics 2009-10-30 P. M. C. de Oliveira , T. J. P. Penna , H. J. Herrmann

A numerical technique is introduced that reduces exponentially the time required for Monte Carlo simulations of non-equilibrium systems. Results for the quasi-stationary probability distribution in two model systems are compared with the…

Adaptation and Self-Organizing Systems · Physics 2009-11-07 A. Bandrivskyy , S. Beri , D. G. Luchinsky , R. Mannella , P. V. E. McClintock

Monte Carlo simulations of diffusion processes often introduce bias in the final result, due to time discretization. Using an auxiliary Poisson process, it is possible to run simulations which are unbiased. In this article, we propose such…

Computational Finance · Quantitative Finance 2016-05-09 Louis Paulot

The study of multiplicity distributions of identified particles in terms of their higher moments is at the focus of contemporary experimental and theoretical studies. In a thermalized system, combinations of these moments are directly…

Nuclear Theory · Physics 2019-02-20 Peter Braun-Munzinger , Anar Rustamov , Johanna Stachel

The Diffusion Monte Carlo method is devoted to the computation of electronic ground-state energies of molecules. In this paper, we focus on implementations of this method which consist in exploring the configuration space with a {\bf fixed}…

Numerical Analysis · Mathematics 2007-05-23 Tony Lelievre , Mohamed El Makrini , Benjamin Jourdain

We introduce a systematic classification method for the analogs of phase transitions in finite systems. This completely general analysis, which is applicable to any physical system and extends towards the thermodynamic limit, is based on…

Statistical Mechanics · Physics 2015-05-28 Stefan Schnabel , Daniel T. Seaton , David P. Landau , Michael Bachmann

The most common way to simplify extensive Monte-Carlo simulations of air showers is to use the thinning approximation. We study its effect on the physical parameters reconstructed from simulated showers. To this end, we have created a…

Astrophysics · Physics 2008-11-26 D. S. Gorbunov , G. I. Rubtsov , S. V. Troitsky

A multicanonical formalism is introduced to describe statistical equilibrium of complex systems exhibiting a hierarchy of time and length scales, where the hierarchical structure is described as a set of nested "internal heat reservoirs"…

Statistical Mechanics · Physics 2012-11-15 Domingos S. P. Salazar , Giovani L. Vasconcelos

In this work we present a theoretical study on the propagation of light in heterogeneous systems with fluctuating optical properties. To understand the consequences of the fluctuations we perform numerical calculations with uniform and non…

Optics · Physics 2015-07-07 Emiliano Terán-Bobadilla , Eugenio Méndez

We present a model for the dynamics in energy space of multicanonical simulation methods that lends itself to a rather complete analytic characterization. The dynamics is completely determined by the density of states. In the \pm J 2D spin…

Statistical Mechanics · Physics 2007-05-23 Miguel D. Costa , J. Viana Lopes , J. M. B. Lopes dos Santos

Numerical simulations of models and theories that describe complex systems such as spin glasses are becoming increasingly important. Beyond fundamental research, these computational methods also find practical applications in fields like…

A diffusion Monte Carlo algorithm is introduced that can determine the correct nodal structure of the wave function of a few-fermion system and its ground-state energy without an uncontrolled bias. This is achieved by confining signed…

Computational Physics · Physics 2020-02-05 Alexander A. Kunitsa , So Hirata

Elastic systems that are spatially heterogeneous in their mechanical response pose special challenges for molecular simulations. Standard methods for sampling thermal fluctuations of a system's size and shape proceed through a series of…

Materials Science · Physics 2015-05-13 Sander Pronk , Phillip L. Geissler

Multicanonical molecular dynamics (MD) is a powerful technique for sampling conformations on rugged potential surfaces such as protein. However, it is notoriously difficult to estimate the multicanonical temperature effectively. Wang and…

Computational Physics · Physics 2007-11-01 Takehiro Nagasima , Akira R. Kinjo , Takashi Mitsui , Ken Nishikawa

We present a new Monte Carlo method for obtaining solutions of the Boltzmann equation for describing phonon transport in micro and nanoscale devices. The proposed method can resolve arbitrarily small signals (e.g. temperature differences)…

Computational Physics · Physics 2015-05-30 Jean-Philippe Peraud , Nicolas Hadjiconstantinou

We recently introduced a novel replica-exchange scheme in which an individual replica can sample from states encountered by other replicas at any previous time by way of a global configuration database, enabling the fast propagation of…

Computational Physics · Physics 2015-12-15 Thomas Vogel , Danny Perez

A Hamiltonian model living in a bounded phase space and with long-range interactions is studied. It is shown, by analytical computations, that there exists an energy interval in which the microcanonical entropy is a decreasing convex…

Statistical Mechanics · Physics 2019-05-01 Fabio Miceli , Marco Baldovin , Angelo Vulpiani

Metropolis Monte Carlo simulation is a powerful tool for studying the equilibrium properties of matter. In complex condensed-phase systems, however, it is difficult to design Monte Carlo moves with high acceptance probabilities that also…

Statistical Mechanics · Physics 2014-05-27 Jerome P. Nilmeier , Gavin E. Crooks , David D. L. Minh , John D. Chodera

This paper introduces a Monte Carlo method for maximum likelihood inference in the context of discretely observed diffusion processes. The method gives unbiased and a.s.\@ continuous estimators of the likelihood function for a family of…

Statistics Theory · Mathematics 2009-03-03 Alexandros Beskos , Omiros Papaspiliopoulos , Gareth Roberts