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transient-conduction
非定常导热,附件包含3个源代码。分别对应着FTCE、BTCS和紧致格式的程序代码(unsteady conduction)
- 2012-01-01 10:05:22下载
- 积分:1
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leapfrog
采用中心差分法,基于蛙跳法计算流体动力学问题(using central-time central-space and leapfrog programe for CFD simulation)
- 2017-04-20 11:42:29下载
- 积分:1
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cov_cal
内有图像协方差的计算,协方差间黎曼距离的计算等等(Within the calculation of the image covariance, the covariance between Riemannian distance calculation, and so on)
- 2020-11-02 15:09:53下载
- 积分:1
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adifdtd1
2D ADI FDTD code.采用不同的三对角矩阵解法(2D ADI FDTD code. Using different tridiagonal matrix method)
- 2020-06-29 19:20:01下载
- 积分:1
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TSVD
电容层析成像TSVD算法的演示实例以及详细的说明(An example of a demonstration of TSVD algorithm for capacitance tomography and a detailed description)
- 2021-04-28 22:18:43下载
- 积分:1
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cycle-model
ABAQUS中的循环模型umat,并附有解释,实现循环加载下的模拟(Cycle model in ABAQUS umat, along with explanation, to achieve the simulation under cyclic loading)
- 2020-11-29 15:09:34下载
- 积分:1
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crackprop_modeI_lefm_xfem_cpe4
crackprop_modeI_lefm_xfem_cpe4,ABAQUS基于线弹性断裂力学的扩展有限元法实例。(crackprop_modeI_lefm_xfem_cpe4 ABAQUS based on linear elastic fracture mechanics extended finite element method instance.)
- 2021-04-08 17:19:00下载
- 积分:1
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petsc-3.0.0-p6.tar
PETSc (Portable, Extensible Toolkit for Scientific Computation) 是美国能源部ODE2000 支持开发的20 多个ACTS 工具箱之一,由Argonne 国家实验室开发的可移植可扩展科学计算工具箱,主要用于在分布式存储环境高效求解偏微分方程组及相关问题。(PETSc (Portable, Extensible Toolkit for Scientific Computation) is ODE2000 U.S. Department of Energy to support the development of one of more than 20 ACTS Toolkit, developed by the Argonne National Laboratory' s Portable, Extensible Toolkit scientific computing, mainly used in the distributed memory efficient environment for solving partial differential equations and related issues.)
- 2009-06-15 19:13:38下载
- 积分:1
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一种优化的神经网络数字预失真方法
说明: 基于神经网络的数字预失真技术,即智能DPD(Digital Predistortion Technology Based on Neural Network)
- 2019-09-27 20:57:50下载
- 积分:1
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spgl1-1.8
based classification (SRC) has been widely used for face
recognition (FR). SRC first codes a testing sample as a
sparse linear combination of all the training samples, and
then classifies the testing sample by evaluating which class
leads to the minimum representation error. While the
importance of sparsity is much emphasized in SRC and
many related works, the use of collaborative representation
(CR) in SRC is ignored by most literature.
- 2013-08-02 16:14:29下载
- 积分:1