大规模集成光波导时域有限元差分法仿真的子域合成法

用时域有限元差分的子域合成法(Synthetic subdomain method of FDTD)对大规模的光波导模拟,把仿真的光波导划分成两个或多个区域,在不影响仿真精度的条件下,删除对仿真对象影响不大的区域,并相应改变吸收边界条件进行时域有限元差分法数值模拟,考查光的传播和损耗状况,并与常规方法的运算结果相比较,结果一致且不影响计算精度。与常规方法相比,两段子域合成法所占内存约为前者的55%,时间约为前者的60%,三段子域合成法所占内存约为前者的31%,时间约为前者的28%,可见子域合成法比常规方法更有利于应用于大规模集成光波导的数值模拟仿真,对节省硬件及时间资源具有实际意义,提高工作效率。     

Vectorization of conjugate-gradient methods for large-scale minimization in meteorology

During the last few years, conjugate-gradient methods have been found to be the best available tool for large-scale minimization of nonlinear functions occurring in geophysical applications. While vectorization techniques have been applied to linear conjugate-gradient methods designed to solve symmetric linear systems of algebraic equations, arising mainly from discretization of elliptic partial differential equations, due to their suitability for vector or parallel processing, no such effort was undertaken for the nonlinear conjugate-gradient method for large-scale unconstrained minimization.Computational results are presented here using a robust memoryless quasi-Newton-like conjugate-gradient algorithm by Shanno and Phua applied to a set of large-scale meteorological problems. These results point to the vectorization of the conjugate-gradient code inducing a significant speed-up in the function and gradient evaluation for the nonlinear conjugate-gradient method, resulting in a sizable reduction in the CPU time for minimizing nonlinear functions of 10⁴ to 10⁵ variables. This is particularly true for many real-life problems where the gradient and function evaluation take the bulk of the computational effort.It is concluded that vector computers are advantageous for largescale numerical optimization problems where local minima of nonlinear functions are to be found using the nonlinear conjugate-gradient method.This research was supported by the Florida State University Supercomputer Computations Research Institute, which is partially funded by the US Department of Energy through Contract No. DE-FC05-85ER250000.     

Janus材料微结构精细调控进展

Janus材料由于其拥有两个具有不同化学组成的表面而备受关注,其特殊的结构和性能已成为材料科学研究热点.如何实现Janus材料形貌可控、化学组成严格分区、不同位点复合功能分区、微结构精细调控和批量化制备是该研究方向中的重点和难点.针对上诉问题,基于本课题组研究工作,本文总结了Janus材料微结构精确控制和批量化制备的方法,为Janus材料大规模制备和应用提供新思路和新方法.     

The Analysis of Network Scale and Reliability in Ring-and-Tree-Based Metro-Access Network with LAN Service

In this article, a (ring-and-tree)-based metro-access network architecture is proposed. By using some conventional optical components, the network can realize passivity in both a remote node and optical network unit (ONU). By employing ring-and-tree topology, the network cannot only provide protection for feeder fibers, but also realize large-scale access. By connecting the ONUs into a ring, the network cannot only carry out mutual protections between ONUs, but also support LAN-service transmission. The analysis of system performance demonstrates the effectiveness of the architecture.     

Renormalization Group Model of Large-Scale Turbulence in Porous Media

Renormalization group methods are used to develop a macroscopic (large-scale) turbulence model for incompressible flow in porous media. The model accounts for the large-distance and large-time behavior of velocity correlations generated by the momentum equation for a randomly stirred, incompressible flow. Utilizing the renormalization procedure, the transport equations for the large-scale modes and expressions for effective transport coefficients are obtained. Expressions for renormalized turbulent viscosity, which accounts for the ultraviolet subrange of the turbulent kinetic energy spectrum, are also obtained.     

Minimization of Linear Functionals Defined on Solutions of Large-Scale Discrete Ill-Posed Problems

The minimization of linear functionals defined on the solutions of discrete ill-posed problems arises, e.g., in the computation of confidence intervals for these solutions. In 1990, Eldén proposed an algorithm for this minimization problem based on a parametric programming reformulation involving the solution of a sequence of trust-region problems, and using matrix factorizations. In this paper, we describe MLFIP, a large-scale version of this algorithm where a limited-memory trust-region solver is used on the subproblems. We illustrate the use of our algorithm in connection with an inverse heat conduction problem. AMS subject classification (2000) 65F22     

一类It(o)型非线性时滞关联随机大系统的分散鲁棒控制

在非线性项满足全局Lipschitz条件下，本文研究了一类Ito型非线性时滞关联随机大系统的分散鲁棒控制问题．系统的时滞是关于状态和控制输入的．基于Lyapunov泛函及线性矩阵不等式（LMI）的分析方法，得到了无记忆状态反馈控制器使整个时滞关联随机大系统可镇定的充分条件．     

Planning under uncertainty using parallel computing

Industry and government routinely solve deterministic mathematical programs for planning and schelduling purposes, some involving thousands of variables with a linear or non-linear objective and inequality constraints. The solutions obtained are often ignored because they do not properly hedge against future contingencies. It is relatively easy to reformulate models to include uncertainty. The bottleneck has been (and is) our capability to solve them. The time is now ripe for finding a way to do so. To this end, we describe in this paper how large-scale system methods for solving multi-staged systems, such as Bender's Decomposition, high-speed sampling or Monte Carlo simulation, and parallel processors can be combined to solve some important planning problems involving uncertainty. For example, parallel processors may make it possible to come to better grips with the fundamental problems of planning, scheduling, design, and control of complex systems such as the economy, an industrial enterprise, an energy system, a water-resource system, military models for planning-and-control, decisions about investment, innovation, employment, and health-delivery systems.