scholarly journals From Atomic Level to Large-Scale Monte Carlo Magnetic Simulations

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3696
Author(s):  
Artur Chrobak ◽  
Grzegorz Ziółkowski ◽  
Dariusz Chrobak ◽  
Grażyna Chełkowska

This paper refers to Monte Carlo magnetic simulations for large-scale systems. We propose scaling rules to facilitate analysis of mesoscopic objects using a relatively small amount of system nodes. In our model, each node represents a volume defined by an enlargement factor. As a consequence of this approach, the parameters describing magnetic interactions on the atomic level should also be re-scaled, taking into account the detailed thermodynamic balance as well as energetic equivalence between the real and re-scaled systems. Accuracy and efficiency of the model have been depicted through analysis of the size effects of magnetic moment configuration for various characteristic objects. As shown, the proposed scaling rules, applied to the disorder-based cluster Monte Carlo algorithm, can be considered suitable tools for designing new magnetic materials and a way to include low-level or first principle calculations in finite element Monte Carlo magnetic simulations.

2019 ◽  
Vol 238 ◽  
pp. 157-164 ◽  
Author(s):  
Artur Chrobak ◽  
Grzegorz Ziółkowski ◽  
Krzysztof Granek ◽  
Dariusz Chrobak

Biometrika ◽  
2020 ◽  
Vol 107 (4) ◽  
pp. 997-1004
Author(s):  
Qifan Song ◽  
Yan Sun ◽  
Mao Ye ◽  
Faming Liang

Summary Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. This paper proposes an extended stochastic gradient Markov chain Monte Carlo algorithm which, by introducing appropriate latent variables, can be applied to more general large-scale Bayesian computing problems, such as those involving dimension jumping and missing data. Numerical studies show that the proposed algorithm is highly scalable and much more efficient than traditional Markov chain Monte Carlo algorithms.


2014 ◽  
Vol 556-562 ◽  
pp. 1584-1587
Author(s):  
Shao Jian Song ◽  
Xiao Han Wang

With the promotion of our country policy about electric vehicles, the development conditions of electric vehicles are improving day by day. However, the application of large-scale electric vehicles will have a direct impact on the power grid. Guilin is taken as an example to analysis the impact of models including uncontrolled charging, controlled charging and controlled charging/discharging model in different scales on the power grid using the Monte Carlo algorithm. The simulation results show that the proper number of electric vehicles involved in the controlled charging/discharging model can effectively achieve the target of peak clipping and valley filling. However, after more than a certain number, it will bring new pressure to the power grid.


2006 ◽  
Vol 73 (2) ◽  
Author(s):  
Natali Gulbahce ◽  
Francis J. Alexander ◽  
Gregory Johnson

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