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Description: 格子Boltzmann方法
格子Boltzmann方法是为了保留格子气自动机方法的优点，克服其缺点而发展起来的方法。
特别是1992年，钱跃弘、陈十一等的开创性工作(提出LBGK模型方法)，使该方法广泛地应用到计算流体力学(单相流、多相流、多孔介质流、热对流、磁流体、反应-扩散等)。
这是“格子模型”的并行处理，在LINUX下调试通过-lattice Boltzmann method lattice Boltzmann method is to retain the lattice gas automata the advantages and overcome its shortcomings and develop ways. In particular, in 1992, Qian Yue Wong Chan 11 the pioneering work (proposed Computational Model), the approach to the wider application of computational fluid dynamics (single-phase flow, multiphase flow, flow in porous media, thermal convection, MHD, the reaction- diffusion, etc.). This is the "lattice model" of parallel processing, the adoption of Linux Debugging
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Size: 17408 |
Author: 张翟 |
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Description: 森林火灾和气体扩散的matlab元胞自动机模拟(cellular Automata simulation)程序。-Forest fires and gas diffusion of matlab simulation of cellular automata (cellular Automata simulation) procedures.
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Size: 1024 |
Author: qinwu |
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Description: 定向扩散（Directed Diffusion, DD）协议在omnet++模拟器上的实现。原理：Sink 向所有传感器节点发送兴趣(兴趣是通过分配不同的属性值来表示不同任务的描述符) , 每个传感器节点在收到兴趣后将其保存在各自的 CACHE 中。每个兴趣项包含一个时间标签域和若干个梯度域。当一个兴趣传遍整个网络后, 从 Source 到 Sink 之间的梯度就建立起来了，梯度反映了网络中节点对匹配请求条件的数据源的近似判断。一旦Source 采集到兴趣所需的数据, 那么它将沿着该兴趣的梯度路径传输数据到汇集点或基站。-Directional proliferation (Directed Diffusion, DD) agreement omnet++ Realize simulator. Principle: Sink to all sensor nodes send interest (interest is through the distribution of different attribute values to express a different mission descriptors), each sensor node after the receipt of interest in the preservation of their respective CACHE. Interest of each tag contains a time domain and a number of gradient domain. When an interest throughout the network, from the Source to Sink gradient between the built up, the gradient reflects the network node on the request of the conditions of matching the data source to determine approximate. Once the Source of interest collected the required data, it would be interested in the gradient along the path to transmit data to the focal point or base station.
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Size: 657408 |
Author: xiaomeihua |
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Description: mani: MANIfold learning demonstration GUI by Todd Wittman, Department of Mathematics, University of Minnesota E-mail wittman@math.umn.edu with comments & questions.
MANI Website: httP://www.math.umn.edu/~wittman/mani/index.html
Last Modified by GUIDE v2.5 10-Apr-2005 13:28:36
Methods obtained from various authors.
(1) MDS -- Michael Lee
(2) ISOMAP -- J. Tenenbaum, de Silva, & Langford
(3) LLE -- Sam Roweis & Lawrence Saul
(4) Hessian LLE -- D. Donoho & C. Grimes
(5) Laplacian -- M. Belkin & P. Niyogi
(6) Diffusion Map -- R. Coifman & S. Lafon
(7) LTSA -- Zhenyue Zhang & Hongyuan Zha
-mani: MANIfold learning demonstration GUI by Todd Wittman, Department of Mathematics, University of Minnesota E-mail wittman@math.umn.edu with comments & questions.
MANI Website: httP://www.math.umn.edu/~wittman/mani/index.html
Last Modified by GUIDE v2.5 10-Apr-2005 13:28:36
Methods obtained from various authors.
(1) MDS-- Michael Lee
(2) ISOMAP-- J. Tenenbaum, de Silva, & Langford
(3) LLE-- Sam Roweis & Lawrence Saul
(4) Hessian LLE-- D. Donoho & C. Grimes
(5) Laplacian-- M. Belkin & P. Niyogi
(6) Diffusion Map-- R. Coifman & S. Lafon
(7) LTSA-- Zhenyue Zhang & Hongyuan Zha
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Size: 14336 |
Author: suxin |
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