Diffusion approximations for multiclass queueing networks under preemptive priority service discipline

We prove a heavy traffic limit theorem to justify diffusion approximations for multiclass queueing networks under preemptive priority service discipline and provide effective stochastic dynamical models for the systems. Such queueing networks appear typically in high-speed integrated services packet networks about telecommunication system. In the network, there is a number of packet traffic types. Each type needs a number of job classes （stages） of processing and each type of jobs is assigned the same priority rank at every station where it possibly receives service. Moreover, there is no inter-routing among different traffic types throughout the entire network.

Global Existence of Smooth Solutions for the Diffusion Approximation Model of General Gas in Radiation Hydrodynamics

In this paper,we consider the 3-D Cauchy problem for the diffusion approximation model in radiation hydrodynamics.The existence and uniqueness of global solutions is proved in perturbation framework,for more general gases including ideal poly tropic gas.Moreover,the optimal time decay rates are obtained for higher-order spatial derivatives of density,velocity,temperature,and radiation field.

Diffusion Approximation of a Multitype Re-entrant Line under Smaller-buffer-first-served Policy

This paper studies a multitype re-entrant line under smaller-buffer-first-served policy, which is an extension of first-buffer-first-served re-entrant line. We prove a heavy traffic limit theorem. The key to the proof is to prove the uniform convergence of the corresponding critical fluid model.

Epidemic modeling:Diffusion approximation vs.stochastic differential equations allowing reflection

A birth-death process is considered as an epidemic model with recovery and transmittance from outside.The fraction of infected individuals is for huge population sizes approximated by a solution of an ordinary differential equation taking values in[0,1].For intermediate size or semilarge populations,the fraction of infected individuals is approximated by a diffusion formulated as a stochastic differential equation.That diffusion approximation however needs to be killed at the boundary{0}U{1}.An alternative stochastic differential equation model is investigated which instead allows a more natural reflection at the boundary.

Modeling Fast Diffusion Processes in Time Integration of Stiff Stochastic Differential Equations

Numerical algorithms for stiff stochastic differential equations are developed using lin-ear approximations of the fast diffusion processes,under the assumption of decoupling between fast and slow processes.Three numerical schemes are proposed,all of which are based on the linearized formulation albeit with different degrees of approximation.The schemes are of comparable complexity to the classical explicit Euler-Maruyama scheme but can achieve better accuracy at larger time steps in stiff systems.Convergence analysis is conducted for one of the schemes,that shows it to have a strong convergence order of 1/2 and a weak convergence order of 1.Approximations arriving at the other two schemes are discussed.Numerical experiments are carried out to examine the convergence of the schemes proposed on model problems.

Optimal Proportional Reinsurance for Controlled Risk Process which is Perturbed by Diffusion

In this paper, we study optimal proportional reinsurance policy of an insurer with a risk process which is perturbed by a diffusion. We derive closed-form expressions for the policy and the value function, which are optimal in the sense of maximizing the expected utility in the jump-diffusion framework. We also obtain explicit expressions for the policy and the value function, which are optimal in the sense of maximizing the expected utility or maximizing the survival probability in the diffusion approximation case. Some numerical examples are presented, which show the impact of model parameters on the policy. We also compare the results under the different criteria and different cases.

Strong Approximations for a Kumar-Seidman Network under a Priority Service Discipline

In this paper,we derive the strong approximations for a four-class two station multi-class queuing network（Kumar-Seidman network） under a priority service discipline.Within a group,jobs are served in the order of arrival,that is,a first-in-first-out disciple,and among groups,jobs are served under a pre-emptiveresume priority disciple.We show that if the input data（i.e.,the arrival and service processe） satisfy an approximation（such as the functional law-of-iterated logarithm approximation or the strong approximation）,the output data（the departure processes） and the performance measures（such as the queue length,the work load and the sojourn time process） satisfy a similar approximation.

Derivation of a Non-Local Model for Diffusion Asymptotics--Application to Radiative Transfer Problems

In this paper,we introduce a moment closure which is intended to provide a macroscopic approximation of the evolution of a particle distribution function,solution of a kinetic equation.This closure is of non local type in the sense that it involves convolution or pseudo-differential operators.We show it is consistent with the diffusion limit and we propose numerical approximations to treat the non local terms.We illustrate how this approach can be incorporated in complex models involving a coupling with hydrodynamic equations,by treating examples arising in radiative transfer.We pay a specific attention to the conservation of the total energy by the numerical scheme.

A novel surrogate modeling strategy of the mechanical properties of 3D braided composites

In this paper,a surrogate-based modeling methodology is developed and presented to predict the elastic properties of three dimensional(3 D)four-directional braided composites.Using this approach,the prediction process becomes feasible with only a limited number of training points.The surrogate models constructed using Finite Element(FE)method and Diffuse Approximation,reduce the computational time and cost for preparing experimental samples.In the FE model,multiscale method is applied to couple the computations of elastic properties at microscale and mesoscale.Subsequently,a parametric study is performed to analyze the effects of the three design parameters on the elastic properties.Satisfactory results are obtained via the surrogatebased modeling predictions,which are compared with the experimental measurements.Moreover,the predictions obtained from surrogate models concur well with the FE predictions.This study orients a new direction for predicting the mechanical properties based on surrogate models which can effectively reduce the sample preparation cost and computational efforts.

Spatio-temporal visualization of light transport in complex photonic structures

Spatio-temporal imaging of light propagation is very important in photonics because it provides the most direct tool available to study the interaction between light and its host environment.Sub-ps time resolution is needed to investigate the fine and complex structural features that characterize disordered and heterogeneous structures,which are responsible for a rich array of transport physics that have not yet been fully explored.A newly developed wide-field imaging system enables us to present a spatiotemporal study on light transport in various disordered media,revealing properties that could not be properly assessed using standard techniques.By extending our investigation to an almost transparent membrane,a configuration that has been difficult to characterize until now,we unveil the peculiar physics exhibited by such thin scattering systems with transport features that go beyond mainstream diffusion modeling,despite the occurrence of multiple scattering.