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We show how to use the multiple histogram method to combine canonical ensemble Monte Carlo simulations made at different temperatures and densities. The method can be applied to study systems of particles with arbitrary interaction…

Statistical Mechanics · Physics 2009-10-31 A. L. Ferreira , M. A. Barroso

Thermal quenching has been used to find metastable materials such as hard steels and metallic glasses. More recently, quenching-based phase control has been applied to correlated electron systems that exhibit metal--insulator, magnetic or…

Materials Science · Physics 2025-04-25 Hiroshi Oike , Hidemaro Suwa , Yasunori Takahashi , Fumitaka Kagawa

We study the efficiency of parallel tempering Monte Carlo technique for calculating true ground states of the Edwards-Anderson spin glass model. Bimodal and Gaussian bond distributions were considered in two and three-dimensional lattices.…

Statistical Mechanics · Physics 2015-05-13 F. Roma , S. Risau-Gusman , A. J. Ramirez-Pastor , F. Nieto , E. E. Vogel

Parallel tempering (PT) methods are a popular class of Markov chain Monte Carlo schemes used to sample complex high-dimensional probability distributions. They rely on a collection of $N$ interacting auxiliary chains targeting tempered…

Computation · Statistics 2021-07-28 Saifuddin Syed , Alexandre Bouchard-Côté , George Deligiannidis , Arnaud Doucet

The specific heat of the Coulomb glass is studied by numerical simulations. Both the lattice model with various strengths of disorder, and the random-position model are considered for the one- to three-dimensional cases. In order to extend…

Disordered Systems and Neural Networks · Physics 2015-06-24 A. Mobius , P. Thomas , J. Talamantes , C. J. Adkins

We present a temperature-independent Monte Carlo method for the determination of the density of states of lattice proteins that combines the fast ground-state search strategy of the nPERM chain growth and multicanonical reweighting for…

Statistical Mechanics · Physics 2009-11-10 Michael Bachmann , Wolfhard Janke

In the present paper we identify a rigorous property of a number of tempering-based Monte Carlo sampling methods, including parallel tempering as well as partial and infinite swapping. Based on this property we develop a variety of…

Statistical Mechanics · Physics 2015-06-11 J. D. Doll , Nuria Plattner , David L. Freeman , Yufei Liu , Paul Dupuis

In this paper we present extensions to the original adaptive parallel tempering algorithm. Two different approaches are presented. In the first one we introduce state-dependent strategies using current information to perform a swap step. It…

Computation · Statistics 2014-06-25 Mateusz Krzysztof Łącki , Błażej Miasojedow

Cross-entropy method model predictive control (CEM--MPC) is a powerful gradient-free technique for nonlinear optimal control, but its performance is often limited by the reliance on random sampling. This conventional approach can lead to…

Systems and Control · Electrical Eng. & Systems 2026-05-12 Markus Walker , Daniel Frisch , Uwe D. Hanebeck

The method of tempered transitions was proposed by Neal (1996) for tackling the difficulties arising when using Markov chain Monte Carlo to sample from multimodal distributions. In common with methods such as simulated tempering and…

Computation · Statistics 2012-09-11 Gundula Behrens , Nial Friel , Merrilee Hurn

We detail the use of simple machine learning algorithms to determine the critical Bose-Einstein condensation (BEC) critical temperature $T_\text{c}$ from ensembles of paths created by path-integral Monte Carlo (PIMC) simulations. We quickly…

Statistical Mechanics · Physics 2019-12-20 Adith Ramamurti

Active thermal control is crucial in achieving the required accuracy and throughput in many industrial applications, e.g., in the medical industry, high-power lighting industry, and semiconductor industry. Thermoelectric Modules (TEMs) can…

Systems and Control · Electrical Eng. & Systems 2020-06-19 Enzo Evers , Rens Slenders , Rob van Gils , Tom Oomen

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

Parameterized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and…

Quantum Physics · Physics 2025-06-04 Conor Smith , Quinn T. Campbell , Tameem Albash

Temperature scaling is a simple method that allows to control the uncertainty of probabilistic models. It is mostly used in two contexts: improving the calibration of classifiers and tuning the stochasticity of large language models (LLMs).…

Machine Learning · Statistics 2026-05-28 Pierre-Alexandre Mattei , Bruno Loureiro

Parallel tempering, or replica exchange, is a popular method for simulating complex systems. The idea is to run parallel simulations at different temperatures, and at a given swap rate exchange configurations between the parallel…

Probability · Mathematics 2016-04-20 J. D. Doll , Paul Dupuis , Pierre Nyquist

This work proposes an extension of phase change and latent heat models for the simulation of metal powder bed fusion additive manufacturing processes on the macroscale and compares different models with respect to accuracy and numerical…

Computational Engineering, Finance, and Science · Computer Science 2021-09-07 Sebastian D. Proell , Wolfgang A. Wall , Christoph Meier

Superfluidity in the cold atomic two-species Fermi gas system in the unitary limit of infinite scattering length remains incompletely understood. In particular, a pseudogap phase has been proposed to exist above the superfluid critical…

Quantum Gases · Physics 2015-06-11 C. N. Gilbreth , Y. Alhassid

In this paper, a hybrid quasi-static atomistic simulation method at finite temperature is developed, which combines the advantages of MD for thermal equilibrium and atomic-scale finite element method (AFEM) for efficient equilibration. Some…

Atomic Physics · Physics 2015-06-17 Ran Xu , Bin Liu

We discuss a rejectionless global optimization technique which, while being technically similar to the recently introduced method of Extremal Optimization, still relies on a physical analogy with a thermalizing system. Our waiting time…

Statistical Mechanics · Physics 2009-11-07 Jesper Dall , Paolo Sibani
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