In various biochemical systems, discrete and stochastic approaches are more appropriate than continuous and deterministic approaches when the system has small numbers of molecules. Before the emergence of the stochastic simulation algorithm (SSA) by Gillespie, chemical master equations were used to stochastically model. Solving the master equations is often mathematically intractable. Therefore...

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A stochastic optimization approach is presented for the remediation design of a contaminated aquifer with limited hydrogeologic information. Stochastic simulation using the Monte Carlo technique, produces a series of equally probable realisations of the spatially varying random hydraulic conductivity field. The stochastic flow and transport simulation model is coupled, using the response matrix...

This paper proposes a stochastic model predictive control (MPC) framework for traffic signal coordination and control in urban traffic networks. One of the important features of the proposed stochastic MPC model is that uncertain traffic demands and stochastic disturbances are taken into account. Aiming to effectively model the uncertainties and avoid queue spillback in traffic networks, we dev...

Abstract. Wind as a resource of renewable energy has obtained an important share of the energy market already. Therefore simulation and prediction of wind speeds is essential for both, for engineers and energy traders. In this paper we analyze the surface wind speed data from three prototypic locations: coastal region (Rotterdam), undulating forest landscape few 100 m above sea level(Kassel), ...

Complex systems display variability over a broad range of spatial and temporal scales. Some scales are unresolved due to computational limitations. The impact of these unresolved scales on the resolved scales needs to be parameterized or taken into account. One stochastic parameterization scheme is devised to take the effects of unresolved scales into account, in the context of solving a nonlin...

The vast majority of stochastic simulation models are imperfect in that they fail to fully emulate the entirety of real dynamics. Despite this, these imperfect models are still useful in practice, so long as one knows how the model is inexact. This inexactness is measured by a discrepancy between the proposed stochastic model and a true stochastic distribution across multiple values of some dec...

Simulation, or more specifically numerical simulation, is a very powerful tool for modeling of engineering, social, business, life science and other systems where the traditional analytical or graphical techniques become very complex, especially if some of the system variables have stochastic nature. Simulation has been applied for solving problems in reliability engineering also. In this paper...

A hybrid multiscale kinetic Monte Carlo (HMKMC) method for speeding up the simulation of copper electrodeposition is presented. The fast diffusion events are simulated deterministically with a heterogeneous diffusion model which considers site-blocking effects of additives. Chemical reactions are simulated by an accelerated (tau-leaping) method for discrete stochastic simulation which adaptivel...