eromanga233

eromanga233's Stars

• pytorch/pytorch

  Tensors and Dynamic neural networks in Python with strong GPU acceleration

  Language:Python84.5k22.8k

• huggingface/transformers

  🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

  Language:Python136k27.1k

• triton-lang/triton

  Development repository for the Triton language and compiler

  Language:C++13.5k1.7k

• ROCm/ROCm

  AMD ROCm™ Software - GitHub Home

  Language:Shell4.7k391

• fajarwr/CNN_3D_Permaebility

  This research is about predicting permeability of the 3D rock images using Convolutional Neural Network.

  Language:Jupyter Notebook73

• yjhp1016/taichi_LBM3D

  A 3D sparse LBM solver implemented using Taichi

  Language:Python28237

• mbandreeta/absolute_permeability_simulations

  Language:Jupyter Notebook135

• kipfstuhl/porous_lbm

  A Lattice Boltzmann Mehtod for incompressible flows in porous media

  Language:Python92

• PorousMediaSimulation/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

  Language:Python8918

• sailfish-team/sailfish

  Lattice Boltzmann (LBM) simulation package for GPUs (CUDA, OpenCL)

  Language:Python23485

• pmocz/latticeboltzmann-python

  Lattice Boltzmann fluid simulation

  Language:Python18150

• nattsr/SRBDRP_Toolbox

  Stanford Rock Physics & Borehole Geophysics (SRB) Digital Rock Physics (DRP) Toolbox. The codes include image creation, alteration, and computation of physical and geometrical properties.

  Language:Matlab2819

• je-santos/MPLBM-UT

  Library for performing multiphase simulations (based on the Shan-Chen model) in complicated geometries (i.e. porous media 3D images)

  Language:Python15259

• PMEAL/OpenPNM

  A Python package for performing pore network modeling of porous media

  Language:Python454174

• SWahidatulhusna/Digital-Rock-Physics

  Calculate permeability, Porosity, Pore Size distributions, Capillary Pressure, Formation Factor and Visualizations

  Language:Jupyter Notebook91

• ImperialCollegeLondon/pnextract

  Pore network extraction from micro-CT images of porous media

  Language:C++5528

• AleksZhuravlyov/pnextract

  Pore network extraction from micro-CT images of porous media

  Language:C++1

• ergosimulation/mpslib

  A C++ class for Mutiple Point Simulation algorithms

  Language:Jupyter Notebook4016

• yasindagasan/MODFLOW-MPS

  Multiple-point statistical simulations & flow simulations for GAN training

  Language:Jupyter Notebook41

• sdyinzhen/MPS-BedMappingV1

  This is the python opensource repo for basal mapping from radar line data using multiple-point geostatistics (MPS)

  Language:Jupyter Notebook81

• UniNE-CHYN/mps_toolbox

  Toolbox for multiple-point statistics

  Language:Python93

• chenzuo789/NearestNeighborSimulation

  A nearest neighbor multiple-point statistics method for fast geological modeling

  Language:Jupyter Notebook91

• labmlai/annotated_deep_learning_paper_implementations

  🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

  Language:Python56.6k5.8k

• UBCDingXin/improved_CcGAN

  Continuous Conditional Generative Adversarial Networks (CcGAN)

  Language:Python12435

• ChatzigeorgiouGroup/FractalDimension

  A python function to calculate the fractal dimension in 3D.

  Language:Jupyter Notebook7122

• georgehalal/cWGAN-GP

  A conditional Wasserstein Generative Adversarial Network with gradient penalty (cWGAN-GP) for stochastic generation of galaxy properties in wide-field surveys

  Language:Python131

• SigCGANs/Conditional-Sig-Wasserstein-GANs

  Language:Python12557

• cameronfabbri/cWGANs

  Conditional Wasserstein GANs

  Language:Python7114

• weiqing1001/CWGAN-for-de-aliased-seismic-interpolation

  Seismic de-aliased interpolation using conditional Wasserstein generative adversarial network.

  Language:Python51

• DIG-Kaust/RockGAN

  Reproducible material for A Wasserstein GAN with gradient penalty for 3D porous media generation.

  Language:Jupyter Notebook286