Monte Carlo Simulation of Diffusion Process on Quasi-Lattice

1992 ◽  
Vol 31 (Part 1, No. 5A) ◽  
pp. 1417-1423 ◽  
Author(s):  
Yasushi Sasajima ◽  
Kazuhiko Sakayori ◽  
Minoru Ichimura ◽  
Mamoru Imabayashi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Diffusion Process

scholarly journals Monte Carlo Methods and New Jump Diffusion Processes and Their Application in Gold Price

2017 ◽  
Author(s):  
Kutluk Kağan Sümer
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Standard Deviation ◽  
Diffusion Process ◽  
Wiener Process ◽  
Mean Reversion ◽  
Jump Diffusion ◽  
Gold Market ◽  
Jump Diffusion Process ◽  
Standard Deviations

This study aimed to execute Monte Carlo simulation method with Wiener Process, Generalized Wiener Process, Mean Reversion Process and Mean Reversion Jump Diffusion Process and to compare them and then expended with the idea of how to include negative and positive news shocks in the gold market to the Monte Carlo simulation. By enhancing the determination of the 3 standard deviation shocks within the process of Classic Mean Jump Diffusion Process, an enchanted model for the 1,96 and 3 standard deviation shocks were being used and additionally positive and negative shocks were added to the system in a different way. This new Mean Reversion Jump Diffusion Process that have been developed by Sümer, executes Monte Carlo simulation regarding the gold market return with five random variables that are chosen from Poisson distribution and one random variable chosen from the normal distribution. Additionally, by accepting volatilities as outlies over the 1,96 and 3 standard deviations with the effect of the new and good news and the standard deviations on the traditional approximate return and the standard deviations (volatility) and the obtained new approximate return and the new standard deviation (volatility) and compares them with the Monte Carlo simulations.

scholarly journals Jump Adapted Scheme Of a Non Mark Dependent Jump Diffusion Process with Application to the Merton Jump Diffusion Model

2017 ◽  
Vol 5 (4) ◽  
pp. 80
Author(s):  
Renaud Fadonougbo ◽  
George O. Orwa
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Diffusion Process ◽  
Strong Convergence ◽  
General Result ◽  
Jump Diffusion ◽  
Discretization Scheme ◽  
Complete Proof ◽  
Jump Diffusion Process ◽  
Jump Diffusion Model

This paper provides a complete proof of the strong convergence of the Jump adapted discretization Scheme in the univariate and mark independent jump diffusion process case. We put in detail and clearly a known and general result for mark dependent jump diffusion process. A Monte-Carlo simulation is used as well to show numerical evidence.

scholarly journals Duality Between the Two-Locus Wright–Fisher Diffusion Model and the Ancestral Process with Recombination

2013 ◽  
Vol 50 (01) ◽  
pp. 256-271 ◽  
Author(s):  
Shuhei Mano
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Diffusion Process ◽  
Closed Form ◽  
Numerical Method ◽  
Diffusion Model ◽  
Ancestral Recombination Graph ◽  
Duality Argument

Known results on the moments of the distribution generated by the two-locus Wright–Fisher diffusion model, and the duality between the diffusion process and the ancestral process with recombination are briefly summarized. A numerical method for computing moments using a Markov chain Monte Carlo simulation and a method to compute closed-form expressions of the moments are presented. By applying the duality argument, the properties of the ancestral recombination graph are studied in terms of the moments.

Monte Carlo simulation on the diffusion of polymer in narrow periodical channels

2017 ◽  
Vol 31 (21) ◽  
pp. 1750144 ◽  
Author(s):  
Ying-Cai Chen ◽  
Yan-Li Zhou ◽  
Chao Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Diffusion Process ◽  
Energy Landscape ◽  
Diffusion Constant ◽  
Channel Interaction ◽  
Physical Mechanisms ◽  
Part Formula ◽  
Projected Length ◽  
Long Time

Diffusion of polymer in narrow periodical channels, patterned alternately into part [Formula: see text] and part [Formula: see text] with the same length [Formula: see text], was studied by using Monte Carlo simulation. The interaction between polymer and channel [Formula: see text] is purely repulsive, while that between polymer and channel [Formula: see text] is attractive. Results show that the diffusion of polymer is remarkably affected by the periodicity of channel, and the diffusion constant [Formula: see text] changes periodically with the polymer length [Formula: see text]. At the peaks of [Formula: see text], the projected length of polymer along the channel is an even multiple of [Formula: see text], and the diffusion of polymer in periodical channel is nearly the same as that of polymer in homogeneous channel. While at the valleys of [Formula: see text], the projected length of polymer is an odd multiple of [Formula: see text], and polymer is in a trapped state for a long time and it rapidly jumps to other trapped regions during the diffusion process. The physical mechanisms are discussed from the view of polymer–channel interaction energy landscape.

scholarly journals Duality Between the Two-Locus Wright–Fisher Diffusion Model and the Ancestral Process with Recombination

2013 ◽  
Vol 50 (1) ◽  
pp. 256-271 ◽  
Author(s):  
Shuhei Mano
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Diffusion Process ◽  
Closed Form ◽  
Numerical Method ◽  
Diffusion Model ◽  
Ancestral Recombination Graph ◽  
Duality Argument

Known results on the moments of the distribution generated by the two-locus Wright–Fisher diffusion model, and the duality between the diffusion process and the ancestral process with recombination are briefly summarized. A numerical method for computing moments using a Markov chain Monte Carlo simulation and a method to compute closed-form expressions of the moments are presented. By applying the duality argument, the properties of the ancestral recombination graph are studied in terms of the moments.

Spatial Diffusion Processes 2: Numerical Analysis

1979 ◽  
Vol 11 (3) ◽  
pp. 335-347 ◽  
Author(s):  
M J Webber ◽  
A E Joseph
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Numerical Analysis ◽  
Diffusion Process ◽  
Diffusion Processes ◽  
Model Solution ◽  
Spatial Diffusion ◽  
System Of Cities ◽  
Reasonable Description ◽  
Parameter Values

An earlier paper (Webber and Joseph, 1978) proposed a model of the process whereby messages diffuse between a system of cities and provided a means of approximating the solution to that model if cities can ‘self-infect’ themselves with the message. This paper continues the analysis of this model by investigating the case in which a city cannot send the message to itself. The analysis is numerical, and an alternative to Monte Carlo simulation is used. The results indicate that the diffusion process described by the model is highly predictable if information on the accessibility of cities is available. A second part of the paper shows that the approximation used in the earlier paper provides a reasonable description of the model solution for at least some parameter values.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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