Results 1 to 10 of about 840,639 (141)

Clock Monte Carlo methods [PDF]

Physical Review E, 2019
We propose the clock Monte Carlo technique for sampling each successive chain step in constant time. It is built on a recently proposed factorized transition filter and its core features include its O(1) computational complexity and its generality.
Michel, Manon, Tan, Xiaojun, Deng, Youjin   +2 more
openaire   +5 more sources

Shell model Monte Carlo methods [PDF]

Physics Reports, 1997
We review quantum Monte Carlo methods for dealing with large shell model problems. These methods reduce the imaginary-time many-body evolution operator to a coherent superposition of one-body evolutions in fluctuating one-body fields; the resultant path integral is evaluated stochastically. We first discuss the motivation, formalism, and implementation
Koonin, S. E., Dean, D. J., Langanke, K.
openaire   +2 more sources

Multispin Monte Carlo Method

Dal'nevostochnyi Matematicheskii Zhurnal, 2020
The article offers a Monte Carlo cluster method for numerically calculating a statistical sample of the state space of vector models. The statistical equivalence of subsystems in the Ising model and quasi-Markov random walks can be used to increase the efficiency of the algorithm for calculating thermodynamic means.
K.V. Makarova, A.G. Makarov, M.A. Padalko, V.S. Strongin, K.V. Nefedev   +4 more
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The Coupled Electronic-Ionic Monte Carlo Simulation Method [PDF]

, 2002
Quantum Monte Carlo (QMC) methods such as Variational Monte Carlo, Diffusion Monte Carlo or Path Integral Monte Carlo are the most accurate and general methods for computing total electronic energies.
B. L. Hammond Jr., B. Militzer, B. Militzer, C. Lin, C. Pierleoni, D. Hohl, D. M. Ceperley, D. M. Ceperley, D. M. Ceperley, D. M. Ceperley, D. M. Ceperley, D. M. Ceperley, D. Marx, D. Saumon, D. Saumon, D. Saumon, I. F. Silvera, J. C. Grossman, J. Kohanoff, J. Kohanoff, M. C. Payne, M. Dewing, M. E. Tuckerman, M. Sprik, N. C. Holmes, N. Metropolis, P. Diep, P. Diep, P. J. Reynolds, R. Car, V. Natoli, V. Natoli, W. J. Nellis, W. M. C. Foulkes, Y. Kwon, Y. Kwon   +35 more
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Numerical approximation of statistical solutions of scalar conservation laws

, 2014
We propose efficient numerical algorithms for approximating statistical solutions of scalar conservation laws. The proposed algorithms combine finite volume spatio-temporal approximations with Monte Carlo and multi-level Monte Carlo discretizations of ...
Barrett, Paul M., Butler, Richard J., Irmis, Randall B., Mundil, Roland, Scheyer, Torsten M., Sánchez-Villagra, Marcelo R.   +5 more
core   +3 more sources

The Monte Carlo Method [PDF]

International Astronomical Union Colloquium, 1971
AbstractWe give here a detailed technical description of a Monte Carlo scheme for the dynamical evolution of spherical stellar systems. The philosophy of the method, as well as a few illustrative results, are given elsewhere (Hénon, 1971, hereafter called I).
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Metropolis Methods for Quantum Monte Carlo Simulations [PDF]

, 2003
Since its first description fifty years ago, the Metropolis Monte Carlo method has been used in a variety of different ways for the simulation of continuum quantum many-body systems.
Ceperley, D. M.
core   +3 more sources

Benchmark of a new multi-ion-species collision operator for $\delta f$ Monte Carlo neoclassical simulation [PDF]

, 2020
A numerical method to implement a linearized Coulomb collision operator in the two-weight $\delta f$ Monte Carlo method for multi-ion-species neoclassical transport simulation is developed.
122862, 123636, 126346, 126422, 67562, 67563, 67564, 67588, Ishiguro, Seiji, KANNO, Ryutaro, MATSUOKA, Seikichi, Nakata, Motoki, NUNAMI, Masanori, Pianpanit, Theerasarn, SATAKE, Shinsuke, SUGAMA, Hideo   +15 more
core   +3 more sources

Optimized Monte Carlo Method for glasses [PDF]

, 2006
A new Monte Carlo algorithm is introduced for the simulation of supercooled liquids and glass formers, and tested in two model glasses. The algorithm is shown to thermalize well below the Mode Coupling temperature and to outperform other optimized Monte ...
Fernandez, L. A., Martin-Mayor, V., Verrocchio, P.   +2 more
core   +6 more sources

Multilevel Monte Carlo methods [PDF]

Acta Numerica, 2013
Monte Carlo methods are a very general and useful approach for the estimation of expectations arising from stochastic simulation. However, they can be computationally expensive, particularly when the cost of generating individual stochastic samples is very high, as in the case of stochastic PDEs.
openaire   +3 more sources