Abstract In this article, we analyze stochastic differential equations model for internal coronavirus (COVID-19) dynamics. The are expressed using the Ito’s formula. Environmental stochasticity in dynamical is presented via parameters disturbance which standard method equations(SDEs) population modeling. We than prove that decided paper have a unique global positive solution because fundamental...

With the development of molecular imaging, Cherenkov optical imaging technology has been widely concerned. Most studies regard partial boundary flux as a stochastic variable and reconstruct images based on steadystate diffusion equation. In this paper, time-variable will be considered radiation emission process regarded process. Based original steady-state equation, we first propose differentia...

Stochastic differential equations that model an SIS epidemic with multiple pathogen strains are derived from a system of ordinary differential equations. The stochastic model assumes there is demographic variability. The dynamics of the deterministic model are summarized. Then the dynamics of the stochastic model are compared to the deterministic model. In the deterministic model, there can be ...

We present a mean field model based on the approach taken by Rempel (2005) in order to investigate the influence of stochastic fluctuations in the Reynolds stresses on meridional flow and differential rotation. The stochastic fluctuations found in the meridional flow pattern directly resemble the stochastic fluctuations of the Reynolds stresses, while the stochastic fluctuations in the differen...

An available method of modeling and predicting the economic time series is the use of stochastic differential equations, which are often determined as jump-diffusion stochastic differential equations in financial markets and underlier economic dynamics. Besides the diffusion term that is a geometric Brownian model with Wiener random process, these equations contain a jump term that follows Pois...

Introduction This chapter is concerned with continuous time processes, which are often modeled as a system of ordinary differential equations. These models assume that the observed dynamics are driven exclusively by internal, deterministic mechanisms. However, real biological systems will always be exposed to influences that are not completely understood or not feasible to model explicitly, and...

the hub location decision is a long term investment and any changes in it take considerable time and money. in real situations, some parameters are uncertain hence, deterministic models cannot be more efficient. the ability of two-stage stochastic programming is to make a long-term decision by considering effects of it in short term decisions simultaneously. in the two-stage stochastic programm...

We study finite element approximations of stochastic partial differential equations of Ginzburg-Landau type and the main paradigm considered in this paper is the stochastic Allen-Cahn model. We first demonstrate that the constructed stochastic finite element approximations are within an arbitrary level of tolerance from the corresponding one-dimensional stochastic partial differential equation;...