fengweiUSTC
A graduate student in University of Science and Technology of China, dedicating to the application of machine learning in fluid mechanics
Pinned Repositories
book
Diffree
Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model
fengweiUSTC.github.io
Elegant theme for Jekyll.
LBM_CUDA_Version
rebuild the fortran version of LBM fluid simulation method to CUDA C version, which accelerate the calculation speed for dozens of times
MegEngine
MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架
openLBMPM
openLBMPM is an open source lattice Boltzmann method (LBM) package for multicomponent and multiphase (MCMP) flow and transport in porous media. Currently, it includes Shan-Chen method and color gradient method for MCMP system. There are two options for Shan-Chen method: (1) Original Shan-Chen method, which integrates the force term to the equilibrium velocity and cannot reach high viscosity ratio; (2) Explicit forcing model developed by M.Porter et al (2012). For color gradient model, the methods developed by Liu et.al (2014), Huang et al (2014) and Takashi et al (2018) are included. For running the codes, CUDA and numba (from Anaconda) are required
wgan-gp
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
fengweiUSTC's Repositories
fengweiUSTC/LBM_CUDA_Version
rebuild the fortran version of LBM fluid simulation method to CUDA C version, which accelerate the calculation speed for dozens of times
fengweiUSTC/book
fengweiUSTC/Diffree
Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model
fengweiUSTC/fengweiUSTC.github.io
Elegant theme for Jekyll.
fengweiUSTC/MegEngine
MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架
fengweiUSTC/openLBMPM
openLBMPM is an open source lattice Boltzmann method (LBM) package for multicomponent and multiphase (MCMP) flow and transport in porous media. Currently, it includes Shan-Chen method and color gradient method for MCMP system. There are two options for Shan-Chen method: (1) Original Shan-Chen method, which integrates the force term to the equilibrium velocity and cannot reach high viscosity ratio; (2) Explicit forcing model developed by M.Porter et al (2012). For color gradient model, the methods developed by Liu et.al (2014), Huang et al (2014) and Takashi et al (2018) are included. For running the codes, CUDA and numba (from Anaconda) are required
fengweiUSTC/wgan-gp
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"