Abstract. Stochastic dynamic analysis of linear or nonlinear multi-degree-of-freedom systems excited by multi-variated processes is usually conducted using digital Monte Carlo (MC) simulation. Since in structural few modal shapes contribute to the response nodal space, computational burden MC simulation mainly related input process. Usually, generation samples Gaussian process performed with ai...

Stochastic models of biological networks are well established in systems biology, where the computational treatment of such models is often focused on the solution of the so-called chemical master equation via stochastic simulation algorithms. In contrast to this, the development of storage-efficient model representations that are directly suitable for computer implementation has received signi...

We have developed a computational framework for accurate and efficient simulation of stochastic spatially inhomogeneous biochemical systems. The new computational method employs a fractional step hybrid strategy. A novel formulation of the finite state projection (FSP) method, called the diffusive FSP method, is introduced for the efficient and accurate simulation of diffusive transport. Reacti...

Cellular automata (CA) play an important role in the development of computationally efficient microscopic traffic models and recently have gained considerable importance as a mean of optimising traffic control strategies. However, real-time application of the available CA models in traffic control systems is a difficult task due to their discrete and stochastic nature. This paper introduces a n...

this paper presents a new optimization algorithm to design an optimal proportional, integral, derivative (pid) controller in hydro-turbine generator governor for damping output frequency oscillations. in this research, we utilize a stochastic and optimal based pid controller to control frequency-response of the hydro turbine. the proposed algorithm is employed to design an optimal pid controlle...