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Many systems in nature and laboratories are far from equilibrium and exhibit significant fluctuations, invalidating the key assumptions of small fluctuations and short memory time in or near equilibrium. A full knowledge of Probability…

Statistical Mechanics · Physics 2017-11-15 Eun-jin Kim , Lucille-Marie Tenk`es , Rainer Hollerbach , Ovidiu Radulescu

We discuss a method to solve models with long-range interactions in the microcanonical and canonical ensemble. The method closely follows the one introduced by Ellis, Physica D 133, 106 (1999), which uses large deviation techniques. We show…

Statistical Mechanics · Physics 2009-11-10 Julien Barre' , Freddy Bouchet , Thierry Dauxois , Stefano Ruffo

There is a great need for improved statistical sampling in a range of physical, chemical and biological systems. Even simulations based on correct algorithms suffer from statistical error, which can be substantial or even dominant when slow…

Computational Physics · Physics 2007-11-09 F. Marty Ytreberg , Daniel M. Zuckerman

We introduce a method to sample the orientational distribution function in computer simulations. The method is based on the exact torque balance equation for classical many-body systems of interacting anisotropic particles in equilibrium.…

Soft Condensed Matter · Physics 2023-04-12 Johannes Renner , Matthias Schmidt , Daniel de las Heras

We present a generic reweighting method for nonequilibrium Markov processes. With nonequilibrium Monte Carlo simulations at a single temperature, one calculates the time evolution of physical quantities at different temperatures, which…

Statistical Mechanics · Physics 2009-11-10 Hwee Kuan Lee , Yutaka Okabe

We study the non-equilibrium work in a pedagogical model of relativistic ideal gas. We obtain the exact work distribution and verify the Jarzynski's equality. In the non-relativistic limit, our results recover the non-relativistic results…

Statistical Mechanics · Physics 2025-11-21 Xianghang Zhang , Tingzhang Shi , H. T. Quan

The configuration model is a sequence of random graphs constructed such that in the large network limit the degree distribution converges to a pre-specified probability distribution. The component structure of such random graphs can be…

Probability · Mathematics 2019-12-12 Shankar Bhamidi , Amarjit Budhiraja , Paul Dupuis , Ruoyu Wu

Situations in many fields of research, such as digital communications, nuclear physics and mathematical finance, can be modelled with random matrices. When the matrices get large, free probability theory is an invaluable tool for describing…

Information Theory · Computer Science 2007-07-13 O. Ryan , M. Debbah

We introduce and study a class of particle hopping models consisting of a single box coupled to a pair of reservoirs. Despite being zero-dimensional, in the limit of large particle number and long observation time, the current and activity…

Statistical Mechanics · Physics 2022-08-31 Yongjoo Baek , Yariv Kafri , Vivien Lecomte

Recent developments in statistical mechanics have allowed the estimation of equilibrium free energies from the statistics of work measurements during processes that drive the system out of equilibrium. Here a different class of processes is…

Statistical Mechanics · Physics 2009-11-11 Artur B. Adib

A theoretical framework is presented for the estimation of nucleation times in systems with Brownian type dynamics. This framework is applied to a prototype system: the two-dimensional Ising model with spin-flip dynamics in an external…

Statistical Mechanics · Physics 2007-05-23 K. Brendel , G. T. Barkema , H. van Beijeren

The density of states of self-gravitational system diverges when the particles are spread to infinity. Other problem based an inhomogeneous distribution of particles,which motivate the gravitational interaction. In this sense the…

Statistical Mechanics · Physics 2009-10-15 B. I. Lev

Validating safety-critical autonomous systems in high-dimensional domains such as robotics presents a significant challenge. Existing black-box approaches based on Markov chain Monte Carlo may require an enormous number of samples, while…

We propose a computational method for large deviation statistics of time-averaged quantities in general Markov processes. In our proposed method, we repeat a response measurement against external forces, where the forces are determined by…

Statistical Mechanics · Physics 2014-03-12 Takahiro Nemoto , Shin-ichi Sasa

We explore the possibility of putting constraints on dark energy models with statistical property of large scale structure in the non-linear region. In particular, we investigate the $w$ dependence of non-Gaussianity of the smoothed density…

Astrophysics · Physics 2010-10-27 Takayuki Tatekawa , Shuntaro Mizuno

Conditional sampling is a fundamental task in Bayesian statistics and generative modeling. Consider the problem of sampling from the posterior distribution $P_{X|Y=y^*}$ for some observation $y^*$, where the likelihood $P_{Y|X}$ is known,…

Methodology · Statistics 2025-10-14 Han Cui , Jingbo Liu

Large deviation principles are established for the Fleming-Viot processes with neutral mutation and selection, and the corresponding equilibrium measures as the sampling rate goes to 0. All results are first proved for the finite allele…

Probability · Mathematics 2016-09-07 Donald Dawson , Shui Feng

We analyse the non-equilibrium distribution in dissipative dynamical systems at finite noise intensities. The effect of finite noise is described in terms of topological changes in the pattern of optimal paths. Theoretical predictions are…

Statistical Mechanics · Physics 2009-11-10 A. Bandrivskyy , S. Beri , D. G. Luchinsky

Non-equilibrium path integral methods for computing quantum free energy differences are applied to a quantum particle trapped in a harmonic well of uniformly changing strength with the purpose of establishing the convergence properties of…

Statistical Mechanics · Physics 2009-11-13 Ramses van Zon , Lisandro Hernandez de la Pena , Gilles H. Peslherbe , Jeremy Schofield

We deal with the problem of gradient estimation for stochastic differentiable relaxations of algorithms, operators, simulators, and other non-differentiable functions. Stochastic smoothing conventionally perturbs the input of a…

Machine Learning · Computer Science 2024-10-11 Felix Petersen , Christian Borgelt , Aashwin Mishra , Stefano Ermon