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Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process. Working with an Euler-Maruyama discretisation for the diffusion, we use variational inference to jointly learn the…

Computation · Statistics 2018-05-15 Thomas Ryder , Andrew Golightly , A. Stephen McGough , Dennis Prangle

Diffusion models achieve strong generation quality, diversity, and distribution coverage, but their performance often comes with expensive inference. In this work, we propose Stochastic Transition-Map Distillation (STMD), a teacher-free…

Machine Learning · Computer Science 2026-05-11 George Rapakoulias , Peter Garud , Lingjiong Zhu , Panagiotis Tsiotras

We report a new multicanonical Monte Carlo (MC) algorithm to obtain the density of states (DOS) for physical systems with continuous state variables in statistical mechanics. Our algorithm is able to obtain an analytical form for the DOS…

Computational Physics · Physics 2017-07-25 Ying Wai Li , Markus Eisenbach

The purpose of this text is: 1) to clarify the foundations of the broad histogram method, stressing the conceptual differences between it and reweighting procedures in general; 2) to propose a very simple microcanonical dynamic rule, yet to…

Condensed Matter · Physics 2007-05-23 Paulo Murilo Castro de Oliveira

We present the molecular hyperdynamics algorithm and its implementation to the nonorthogonal tight-binding model NTBM and the corresponding software. Due to its multiscale structure, the proposed approach provides the long time scale…

In this paper, we study the thermodynamics of short-range central potentials, namely, the Lee-Wick potential, and the Plasma potential. In the first part of the paper we obtain the numerical solution for the orbits equation for these…

General Relativity and Quantum Cosmology · Physics 2020-09-11 M. J. Neves , Everton M. C. Abreu , Jorge B. de Oliveira , Marcelo Kesseles Gonçalves

We devise a hierarchy of computational algorithms to enumerate the microstates of a system comprising N independent, distinguishable particles. An important challenge is to cope with integers that increase exponentially with system size,…

Computational Physics · Physics 2015-05-28 Trisha Salagaram , Nithaya Chetty

The design and the simulation of a borehole-heat-exchanger (BHE) field is usually performed by simplified methods that yield either an overestimation or an underestimation of the thermal response. The methods employing the assumption of a…

Applied Physics · Physics 2024-12-11 Enzo Zanchini , Francesco Zanchini

We examine the non-extensive approach to the statistical mechanics of Hamiltonian systems with $H=T+V$ where $T$ is the classical kinetic energy. Our analysis starts from the basics of the formalism by applying the standard variational…

Statistical Mechanics · Physics 2015-05-28 J. F. Lutsko , J. P. Boon

We present an efficient sampling method for computing a partition function and accelerating configuration sampling. The method performs a random walk in the $\lambda$ space, with $\lambda$ being any thermodynamic variable that characterizes…

Computational Physics · Physics 2010-03-02 Cheng Zhang , Jianpeng Ma

The context of the present paper is stochastic thermodynamics - an approach to nonequilibrium thermodynamics rooted within the broader framework of stochastic control. In contrast to the classical paradigm of Carnot engines, we herein…

Systems and Control · Electrical Eng. & Systems 2021-01-08 Rui Fu , Olga Movilla Miangolarra , Amirhossein Taghvaei , Yongxin Chen , Tryphon T. Georgiou

We review the stochastic Gross-Pitaevskii approach for non-equilibrium finite temperature Bose gases, focussing on the formulation of Stoof; this method provides a unified description of condensed and thermal atoms, and can thus describe…

Quantum Gases · Physics 2017-08-23 Stuart P. Cockburn , Nick P. Proukakis

Non-extensive systems do not allow to go to the thermodynamic limit. Therefore we have to reformulate statistical mechanics without invoking the thermodynamical limit. I.e. we have to go back to Pre-Gibbsian times. We show that Boltzmann's…

Statistical Mechanics · Physics 2007-05-23 D. H. E. Gross

A multiscale numerical solver called the seamless-domain method (SDM) is used in linear heat conduction analysis of nonperiodic simulated fields. The practical feasibility of the SDM has been verified for use with periodic fields but has…

Numerical Analysis · Mathematics 2019-12-18 Yoshiro Suzuki

We report microcanonical Monte Carlo simulations of melting and superheating of a generic, Lennard-Jones system starting from the crystalline phase. The isochoric curve, the melting temperature $T_m$ and the critical superheating…

Statistical Mechanics · Physics 2017-03-20 Sergio Davis , Gonzalo Gutiérrez

Microcanonical equations for several thermodynamic properties of a system, suitable for molecular dynamics simulations, are derived from the nonextensive Tsallis entropy functional. Two possible definitions of temperature, the usual one and…

Statistical Mechanics · Physics 2011-03-21 J. Carrete , L. M. Varela , L. J. Gallego

Cryo-EM data processing typically focuses on the structure of the main conformational state under investigation and discards images that belong to other states. This approach can reach atomic resolution, but ignores vast amounts of valuable…

Biomolecules · Quantitative Biology 2024-02-20 Felix Lambrecht , Andreas Kröpelin , Mario Lüttich , Michael Habeck , David Haselbach , Holger Stark

The assessment of the thermal properties of walls is essential for accurate building energy simulations that are needed to make effective energy-saving policies. These properties are usually investigated through in-situ measurements of…

Applications · Statistics 2017-09-21 Marco Iglesias , Zaid Sawlan , Marco Scavino , Raul Tempone , Christopher Wood

In this paper physical multi-scale processes governed by their own principles for evolution or equilibrium on each scale are coupled by matching the stored and dissipated energy, in line with the Hill-Mandel principle. In our view the…

Statistics Theory · Mathematics 2019-12-09 M. S. Sarfaraz , B. Rosic , H. G. Matthies , A. Ibrahimbegovic

We present a Bayesian non-parametric way of inferring stochastic differential equations for both regression tasks and continuous-time dynamical modelling. The work has high emphasis on the stochastic part of the differential equation, also…

Machine Learning · Statistics 2020-06-29 Martin Jørgensen , Marc Peter Deisenroth , Hugh Salimbeni
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