sashahexe

sashahexe's Stars

• tinygrad/tinygrad

  You like pytorch? You like micrograd? You love tinygrad! ❤️

  Language:Python26.9k2717973k

• Sanster/IOPaint

  Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.

  Language:Python19.5k1434592k

• microsoft/nni

  An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

  Language:Python14.1k2842.1k1.8k

• OpenMined/PySyft

  Perform data science on data that remains in someone else's server

  Language:Python9.5k1983.4k2k

• satellite-image-deep-learning/techniques

  Techniques for deep learning with satellite & aerial imagery

  8.7k278231.5k

• thunil/Physics-Based-Deep-Learning

  Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

  1.7k1266304

• tum-pbs/PhiFlow

  A differentiable PDE solving framework for machine learning

  Language:Python1.5k2685192

• Huage001/PaintTransformer

  Officially unofficial re-implementation of paper: Paint Transformer: Feed Forward Neural Painting with Stroke Prediction, ICCV 2021.

  Language:Python4909974

• PMEAL/OpenPNM

  A Python package for performing pore network modeling of porous media

  Language:Python453341.6k174

• neuraloperator/physics_informed

  Language:Python36692985

• benmoseley/FBPINNs

  Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

  Language:Python312122264

• nanditadoloi/PINN

  Simple PyTorch Implementation of Physics Informed Neural Network (PINN)

  Language:Jupyter Notebook2544447

• LukasMosser/PorousMediaGan

  Reconstruction of three-dimensional porous media using generative adversarial neural networks

  Language:Jupyter Notebook179171364

• AmeyaJagtap/XPINNs

  Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

  1536144

• Photon-AI-Research/NeuralSolvers

  Neural network based solvers for partial differential equations and inverse problems :milky_way:. Implementation of physics-informed neural networks in pytorch.

  Language:Python141124446

• isds-neu/PhyCRNet

  Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs

  Language:Python12352035

• PredictiveIntelligenceLab/MultiscalePINNs

  Language:Python1159732

• shawnrosofsky/PINO_Applications

  Applications of PINOs

  Language:Jupyter Notebook1093425

• lanl/MF-LBM

  MF-LBM: A Portable, Scalable and High-performance Lattice Boltzmann Code for DNS of Flow in Porous Media

  Language:Fortran1028027

• Franjcf/hybridPorousInterFoam

  OpenFOAM solver for performing single- and two-phase flow simulations on hybrid-scale porous media.

  Language:C++9210936

• amir-cardiolab/PINN-examples

  Examples implementing physics-informed neural networks (PINN) in Pytorch

  Language:Python543213

• csoulain/porousMedia4Foam

  Language:C++492520

• JAX-DIPS/JAX-DIPS

  JAX-DIPS is a differentiable interfacial PDE solver.

  Language:Python40601

• AmeyaJagtap/Locally-Adaptive-Activation-Functions-Neural-Networks-

  Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawaguchi, G E Karniadakis, Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 20200334, 2020. (http://dx.doi.org/10.1098/rspa.2020.0334)".

  Language:Python383112

• sorush-khajepor/listLBM

  ListLBM is a sparse lattice Boltzmann solver for multiphase flow in porous media

  Language:Fortran372110

• CognitiveModeling/finn

  The public repository about our joint FINN research project

  Language:Python364015

• akapet00/locally-adaptive-activation-functions

  Simplified implementation of locally adaptive activation functions (LAAF) with slope recovery for deep and physics-informed neural networks (PINNs) in PyTorch.

  Language:Python30307

• AmeyaJagtap/Error_estimates_PINN_and_XPINN_NonlinearPDEs

  The first comprehensive theoretical analysis of PINNs (and XPINNs) for a prototypical nonlinear PDE, the Navier-Stokes equations are given.

  7200

• LanPeng-94/DD-PINNs-RRE

  Language:Jupyter Notebook2100

• AmeyaJagtap/Activation-functions-in-regression-and-classification

  How important are How important are activation functions in regression and classification? A survey, performance comparison, and future directions

  1200