the stochastic reaction diffusion systems may suffer sudden shocks‎, ‎in order to explain this phenomena‎, ‎we use markovian jumps to model stochastic reaction diffusion systems‎. ‎in this paper‎, ‎we are interested in almost sure exponential stability of stochastic reaction diffusion systems with markovian jumps‎. ‎under some reasonable conditions‎, ‎we show that the trivial solution of stocha...

By adopting a real options framework, we develop and analyze a production control model that jointly incorporates process and market risks. In our model, process risk is typified by random yield variability while market risk is defined through demand uncertainty. The stochastic processes used to depict uncertainty in these state variables reflect a wide variety of distributional forms and are n...

This paper addresses the strategic planning, design and optimization of a network of petrochemical processes under uncertainty and risk considerations. The problem was formulated as a two-stage stochastic mixed-integer nonlinear programming model (MINLP) with parameter uncertainty considered in process yield, raw material and product prices, and upper and lower product market demand. Risk was a...

The mine production planning defines a sequence of block extraction to obtain the highest NPV under a number of constraints. Mathematical programming has become a widespread approach to optimize production planning, for open pit mines since the 1960s. However, the previous and existing models are found to be limited in their ability to explicitly incorporate the ore grade uncertainty into the p...

We investigate smooth approximations of functions, with prescribed gradient behavior on a distinguished stratified subset of the domain. As an application, we outline how our results yield important consequences for a recently introduced class of stochastic processes, called the matrix-valued Bessel processes.

We investigate smooth approximations of functions, with prescribed gradient behavior on a distinguished stratifiable subset of the domain. As an application, we outline how our results yield important consequences for a recently introduced class of stochastic processes, called the matrix-valued Bessel processes.

Gauge invariant density-density correlators yield detailed information on hadron structure. Hadron deformation and form factors can be extracted for momentum transfers up to about 6 GeV 2. We use stochastic techniques and dilution to compute the all to all propagator required for the exact evaluation of density-density correlators. We present first results for the pion form factor.

In this paper, we derive a new class of finite-dimensional filters for integrals and stochastic integrals of moments of the state for continuous-time linear Gaussian systems. Apart from being of significant mathematical interest, these new filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the model parameters.

We consider model order reduction of stochastic linear systems by balanced truncation. Two types of Gramians are suggested, both satisfying generalized Lyapunov equations. The first is motivated by energy functionals, the second is taylored to yield an error bound for the truncated system.

Combinatorial formulas for the moments of the Brownian motion on classical compact Lie groups are obtained. These expressions are deformations of formulas of B. Collins and P. Śniady for moments of the Haar measure and yield a proof of the First Fundamental Theorem of Invariant and of classical Schur-Weyl dualities based on stochastic calculus.