Monte Carlo simulations in statistical physics

By Dietrich Stauffer. 1988

Subfields: Numerical Methods & Scientific Computing, AI for Physical Sciences

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Citations in the corpus

Learning Energy-Based Spatial-Temporal Generative ConvNets for Dynamic Patterns (2019, methodology): “This treatment is similar to the lattice field theory in physics, which is also based on a Gibbs distribution whose energy is defined on the spatial-temporal domain, and the MCMC sampler evolves the system over a virtue time [64].”
Markov Chain Monte Carlo Simulations and Their Statistical Analysis (2004, methodology): “Estimates of z are compiled in the book by Landau and Binder [16].”
Growth of Epitaxial Graphene: Theory and Experiment (2014, unknown): “In Monte Carlo methods [214] the deterministic equations (7) of MD are replaced by equations based on stochastic transitions for the slow processes in the system.”
Collective states of interacting ferromagnetic nanoparticles (2005, methodology): “Ensembles of dipolarly interacting nanoparticles were studied by Monte-Carlo simulations with the Metropolis algorithm [ 20 ].”
Parton distributions and lattice QCD calculations: a community white paper (2017, methodology): “2 Lattice QCD The lattice-QCD method is based on regularizing QCD on a finite Euclidean lattice and is generally studied by numerical computation of QCD correlation functions in the path-integral formalism [31,76– 78], using methods adapted from statistical mechanics [79, 80].”
Explosive transitions in complex networks’ structure and dynamics: Percolation and synchronization (2016, background): “Therefore, one of the most generally accepted (and simplest) methods to verify whether or not an observed transition is first-order like is the existence of a hysteresis loop [114, 115].”
Gauge bosons at zero and finite temperature (2011, background): “It is known from solid-state physics [147] and lower-dimensional QED [148] that these can be easily misleading.”