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lbm
格子玻尔兹曼模型DnQm系列模型中应用较为广泛的D2Q7模型源代码，用于模拟流体的流动。(The D2Q7 model source code of the the Lattice Boltzmann the model DnQm series model is widely used to simulate fluid flow.)
- 2012-10-27 00:03:05下载
- 积分：1
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RationalComplex Matrix calculator
说明： Matrix multiplication explained in detail for C++
- 2020-08-31 03:09:28下载
- 积分：1
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Shepard-fitting-
地理数据拟合，用于GNSS高程拟合，内附示例(Geographic data fitting, used in GNSS elevation fitting, enclose the sample
)
- 2021-01-27 19:08:41下载
- 积分：1
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registration
point cloud 点云配准计算，通过选择几组匹配的点云数据对对点云数据进行配准处理(point cloud point cloud registration computing, by selecting a set of match point cloud data point cloud data registration process)
- 2016-01-12 11:03:05下载
- 积分：1
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fortran-files
文件1.f90：生成翼型naca0012的椭圆网格，并计算流场，给出壁面压力分布
文件2.f90：maccormack方法解一维burger s方程
文件3.f90：解一维laval管流动，其进出口均为亚音速，喉道后部有激波(File 1.f90: generate the airfoil naca0012 elliptical rotary cell, and calculate the flow field, given the wall pressure distribution file 2.f90: maccormack method to solve the burger' s equation, one-dimensional file 3.f90: solution of one-dimensional laval tube flow, its progress exports are subsonic, the back of the throat have shock)
- 2012-06-10 18:21:15下载
- 积分：1
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creep
abaqus蠕变子程序,user中的实例(creep subroutine for abaqus user)
- 2021-04-01 22:29:07下载
- 积分：1
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An-Introduction-to-Statistical
统计学习的入门书，通俗易懂，号称是ESL的入门版，全书没有太多数学推导，适合学工程的人不适合学统计的人读(Introduction to statistical learning books, easy to understand, known as the entry version of ESL, the book does not have too many mathematical derivations, suitable for people who are not suitable for learning the project to read statistics.)
- 2016-05-04 11:28:37下载
- 积分：1
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mosqp
MATLAB，多目标优化，使用SQP算法(MATLAB, multi-objective optimization using SQP Algorithm)
- 2017-02-14 09:18:47下载
- 积分：1
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szpGauss
用C＋＋实现的高斯混合模型的算法类，方差矩阵是对角矩阵(C++ Gaussian mixture model algorithm category, variance matrix is diagonal matrix)
- 2005-08-09 16:32:03下载
- 积分：1
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hartigansSLC_OpenCV
hartigans Sequential Leader Clustering Algorithm in terms of OpenCV (ver.1.1)
Sequential Leader algorithm:
Hartigan, J. A. (1975), Clustering Algorithms. John Wiley and Sons, Inc., New York, NY.
1. Select maximum cluster "radius"
2. Start with zero clusters
3. Add each item to be clustered to:
* Closest cluster if distance <= r
* New cluster if distance to closest cluster > r
4. Compute new center from members in cluster
5. Empty the clusters (keeping the centers)
6. Return to step 3 (until no changes are made)
- 2010-02-25 19:28:25下载
- 积分：1
