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Monte Carlo Simulation in Statistical Physics: An Introduction
Kurt Binder,Dieter W. Heermann +1 more
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TLDR
A short and systematic theoretical introduction to the Monte Carlo method and a practical guide with plenty of examples and exercises for the student.Abstract:
Introduction - purpose and scope of this volume, and some general comments theoretical foundation of the Monte Carlo method and its application in statistical physics guide to practical work with the Monte Carlo method some important recent developments of the Monte Carlo methodology.read more
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Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering
TL;DR: A new method for detecting and sorting spikes from multiunit recordings that combines the wave let transform with super paramagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions is introduced.
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Bayesian Computation and Stochastic Systems
TL;DR: Basic methodology of MCMC is presented, emphasizing the Bayesian paradigm, conditional probability and the intimate relationship with Markov random fields in spatial statistics, and particular emphasis on the calculation of posterior probabilities.
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Applications of Monte Carlo methods to statistical physics
TL;DR: An introductory review of the Monte Carlo method for the statistical mechanics of condensed matter systems is given in this paper, where basic principles (random number generation, simple sampling versus importance sampling, Markov chains and master equations) are explained and some classical applications (self-avoiding walks, percolation, the Ising model) are sketched.
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Atomistic modeling of interfaces and their impact on microstructure and properties
Yuri Mishin,Mark Asta,Ju Li +2 more
TL;DR: An overview of the most recent developments in the area of atomistic modeling with emphasis on interfaces and their impact on microstructure and properties of materials is given in this paper, along with some challenges and future research directions in this field.
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A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials
TL;DR: A state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world's largest supercomputers is discussed that can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations.