Simulating Stochastic Diffusion Processes and Processes with “Market” Time

Simulating stochastic processes with OMNeT++

Modeling of correlated traffic processes is a challenge in simulation modeling but is necessary to obtain realistic simulation models of many computer or communication networks. We present a new simple module denoted as ArrivalProcess to be included as a traffic source in OMNeT++ simulation models. The new traffic source supports different stochastic process models which are defined using a XML...

Simulating non-Markovian stochastic processes.

We present a simple and general framework to simulate statistically correct realizations of a system of non-Markovian discrete stochastic processes. We give the exact analytical solution and a practical and efficient algorithm like the Gillespie algorithm for Markovian processes, with the difference being that now the occurrence rates of the events depend on the time elapsed since the event las...

Diffusion Equation and Stochastic Processes.

Quantum Recurrent Networks for Simulating Stochastic Processes

We introduce the concept of quantum recurrent networks by incorporating classical feedback loops into conventional quantum networks. We show that the dynamical evolution of such networks, which interleave quantum evolution with measurement and reset operations, exhibit novel dynamical properties finding application in pattern recognition, optimization and simulation. Moreover, decoherence in qu...

Modeling and Simulating Biological Processes with Stochastic Multiset Rewriting

Membrane systems are models of computation inspired by the structure and the function of biological cells. The model was introduced in 1998 by Gh. Păun and since then many results have been obtained, mostly concerning the computational power of the model (for an updated bibliography the reader can consult the web-page [24]). More recently, membrane systems have been applied to systems biology a...