Kinematics of stochastic diffusion processes

2005 ◽  
pp. 22-59
Keyword(s):  
Diffusion Processes ◽  
Stochastic Diffusion

scholarly journals Operational Efficiency Forecasting Model of an Existing Underground Mine Using Grey System Theory and Stochastic Diffusion Processes

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Svetlana Strbac Savic ◽  
Jasmina Nedeljkovic Ostojic ◽  
Zoran Gligoric ◽  
Cedomir Cvijovic ◽  
Snezana Aleksandrovic
Keyword(s):  
Time Horizon ◽  
Diffusion Processes ◽  
Operational Efficiency ◽  
Grey System Theory ◽  
Underground Mine ◽  
Production Costs ◽  
Forecasting Model ◽  
Grey System ◽  
Stochastic Diffusion ◽  
Operating Leverage

Forecasting the operational efficiency of an existing underground mine plays an important role in strategic planning of production. Degree of Operating Leverage (DOL) is used to express the operational efficiency of production. The forecasting model should be able to involve common time horizon, taking the characteristics of the input variables that directly affect the value of DOL. Changes in the magnitude of any input variable change the value of DOL. To establish the relationship describing the way of changing we applied multivariable grey modeling. Established time sequence multivariable response formula is also used to forecast the future values of operating leverage. Operational efficiency of production is often associated with diverse sources of uncertainties. Incorporation of these uncertainties into multivariable forecasting model enables mining company to survive in today’s competitive environment. Simulation of mean reversion process and geometric Brownian motion is used to describe the stochastic diffusion nature of metal price, as a key element of revenues, and production costs, respectively. By simulating a forecasting model, we imitate its action in order to measure its response to different inputs. The final result of simulation process is the expected value of DOL for every year of defined time horizon.

scholarly journals Drilling head knives degradation modelling based on stochastic diffusion processes backed up by state space models

2022 ◽  
Vol 166 ◽  
pp. 108448
Author(s):  
David Vališ ◽  
Jakub Gajewski ◽  
Marie Forbelská ◽  
Zdeněk Vintr ◽  
Józef Jonak
Keyword(s):  
State Space ◽  
Diffusion Processes ◽  
State Space Models ◽  
Stochastic Diffusion

Accelerated stochastic diffusion processes

1990 ◽  
Vol 147 (4) ◽  
pp. 168-174 ◽  
Author(s):  
Piotr Garbaczewski
Keyword(s):  
Diffusion Processes ◽  
Stochastic Diffusion

scholarly journals Cosmic mass functions from Gaussian stochastic diffusion processes

2001 ◽  
Vol 370 (3) ◽  
pp. 715-728 ◽  
Author(s):  
P. Schuecker ◽  
H. Böhringer ◽  
K. Arzner ◽  
T. H. Reiprich
Keyword(s):  
Diffusion Processes ◽  
Stochastic Diffusion ◽  
Mass Functions ◽  
Cosmic Mass

Simulating Stochastic Diffusions by Quantum Walks

2013 ◽  
Author(s):  
Yan Wang
Keyword(s):  
Monte Carlo ◽  
State Space ◽  
Path Integral ◽  
Diffusion Processes ◽  
Planck Equation ◽  
Quantum Walk ◽  
Quantum Walks ◽  
Step Size ◽  
Stochastic Diffusion ◽  
Time Quantum

Stochastic differential equation (SDE) and Fokker-Planck equation (FPE) are two general approaches to describe the stochastic drift-diffusion processes. Solving SDEs relies on the Monte Carlo samplings of individual system trajectory, whereas FPEs describe the time evolution of overall distributions via path integral alike methods. The large state space and required small step size are the major challenges of computational efficiency in solving FPE numerically. In this paper, a generic continuous-time quantum walk formulation is developed to simulate stochastic diffusion processes. Stochastic diffusion in one-dimensional state space is modeled as the dynamics of an imaginary-time quantum system. The proposed quantum computational approach also drastically accelerates the path integrals with large step sizes. The new approach is compared with the traditional path integral method and the Monte Carlo trajectory sampling.

scholarly journals Stochastic diffusion processes on Cartesian meshes

2016 ◽  
Vol 294 ◽  
pp. 1-11 ◽  
Author(s):  
Lina Meinecke ◽  
Per Lötstedt
Keyword(s):  
Diffusion Processes ◽  
Stochastic Diffusion ◽  
Cartesian Meshes

Trend analysis using nonhomogeneous stochastic diffusion processes. Emission of CO2; Kyoto protocol in Spain

2006 ◽  
Vol 22 (1) ◽  
pp. 57-66 ◽  
Author(s):  
R. Gutiérrez ◽  
R. Gutiérrez-Sánchez ◽  
A. Nafidi
Keyword(s):  
Trend Analysis ◽  
Kyoto Protocol ◽  
Diffusion Processes ◽  
Stochastic Diffusion

Certain problems with the application of stochastic diffusion processes for description of chemical engineering phenomena; Modelling of processes by means of stochastic differential equations

1988 ◽  
Vol 53 (7) ◽  
pp. 1500-1518 ◽  
Author(s):  
Vladimír Kudrna
Keyword(s):  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Chemical Engineering ◽  
Diffusion Processes ◽  
Transport Equations ◽  
Diffusion Equations ◽  
Stochastic Diffusion ◽  
Direct Use

The paper points at certain problems associated with direct use of stochastic differential equations for description of chemical engineering processes or with the use of corresponding diffusion equations. It is shown that on the basis of various definitions one can write down three types of stochastic differential equations which might, in principle, describe the same process. One of these types is at the same time equivalent to the classic transport equations common in chemical engineering. A method is described removing these inconsistencies.

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