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周六 / MusicFree forked from 猫头猫 / MusicFree

插件化、定制化、无广告的免费音乐播放器

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周六 / MatlabPNM

A Matlab-based package for performing Pore Network Modeling of porous media 此为从GitHub上fork科学家Mohammad Hossein Golestan并导入到我的Gitee中的资料，源文件：https://github.com/mhgolestan/MatlabPNM

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周六 / cgal

The public CGAL repository, see the README below

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周六 / featool-multiphysics

FEATool Multiphysics - "Physics Simulation Made Easy" (Fully Integrated FEA, FEniCS, OpenFOAM, SU2 Solver GUI & Simulation Platform)

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周六 / OpenPNM

A Python package for performing pore network modeling of porous media

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周六 / DFNGenerator

DFN Generator

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周六 / porepy

Python Simulation Tool for Fractured and Deformable Porous Media

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周六 / GOSIM_MPSAlgorithm

An EM-like optimization algorithm for spatial pattern learning and reproduction for Multiple point statistics simulation

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周六 / opm-simulators

Simulator programs and utilities for automatic differentiation.

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周六 / Petroleum-DS-ML-with-Python

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周六 / pyMRST

A wrapper to run MATLAB Reservoir Simulation Toolbox (MRST) in Python

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周六 / riftXFEM

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周六 / gmtsar

GMTSAR

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周六 / Interactive_Relative_Permeability

Creates Water-Oil and Gas-Liquid relative permeability curves with interactive gas relative perm parameters. Outputs relative perm tables needed for reservoir simulators.

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周六 / Force-2020-Machine-Learning-competition

the results, code and the data for the Force 2020 Machine learning competition after the completion of the competition in October 2020.

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周六 / AsFem

A Simple Finite Element Method program (AsFem)

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周六 / oneDemension-Finite-Element-Codes-Matlab

A simple Matlab implementation for 1D finite element methods for different physical phenomena: Heat transfer (linear/non-linear, steady-state/transient), wave equation (elastodynamics), and Coupled thermo-mechanics.

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周六 / manifem

Finite element library in C++

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周六 / BP_neural_network

采用的数据集著名的“MNIST数据集”完成一个神经网络的训练和测试，不允许使用tensorflow等框架。并用两种不同的bp模型做性能对比 （比如一个层数和神经元较少的简单模型和一个层数和神经元较多的复杂模型）。

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周六 / ResNeXt

Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks