weishanlee

weishanlee's Stars

• rezaakb/pinns-tf2

  PINNs-TF2, Physics-informed Neural Networks (PINNs) implemented in TensorFlow V2.

  Language:Python8627

• skoohy/GPT-PINN

  Generative Pre-Trained Physics-Informed Neural Networks Implementation

  Language:Python8116

• 314arhaam/burger-pinn

  A Physics-Informed Neural Network for solving Burgers' equation.

  Language:Jupyter Notebook298

• karpathy/micrograd

  A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

  Language:Jupyter Notebook10.7k1.5k

• erikgahner/awesome-ggplot2

  A curated list of awesome ggplot2 tutorials, packages etc.

  1.6k169

• continue-revolution/sd-webui-animatediff

  AnimateDiff for AUTOMATIC1111 Stable Diffusion WebUI

  Language:Python3.1k262

• AUTOMATIC1111/stable-diffusion-webui

  Stable Diffusion web UI

  Language:Python145k27.2k

• Stability-AI/generative-models

  Generative Models by Stability AI

  Language:Python24.9k2.8k

• hacheyz/PMMAA

  Python 数学建模算法与应用，笔记与代码，按章节整理

  Language:Jupyter Notebook22427

• conda-forge/miniforge

  A conda-forge distribution.

  Language:Shell6.8k342

• phaseportrait/phaseportrait

  A simple way to do 2D and 3D phase portraits.

  Language:Python366

• QijingZheng/pyband

  band plot using python matplotlib

  Language:Python15593

• chjch/pylusat

  Python for Land Use Suitability Analysis Tools

  Language:Jupyter Notebook142

• michstrauch/CoMOLA

  CoMOLA is a generic python tool for Constrained Multi-objective Optimization of Land use Allocation. It offers a framework to explore a landscape’s potential for ecosystem service supply and biodiversity conservation considering land use conversion and areal coverage constraints. CoMOLA can be used immediately by inputting a raster map representing the status-quo land use, ready-to-run models written in R including their input data, and (optional) information on constraints. The end product is a set of pareto-optimal solutions (land use maps) from which decision makers can discuss and select appropriate solutions according to their preferences.

  Language:Python2813

• dr-kinder/code-samples

  Code samples for "A Student's Guide to Python for Physical Modeling"

  Language:Python1413

• PacktPublishing/Tensorflow-2.0-Computer-Vision-Cookbook

  Tensorflow 2.0 Computer Vision Cookbook, published by Packt

  Language:Python5041

• CSchoel/nolds

  Nonlinear measures for dynamical systems (based on one-dimensional time series)

  Language:Python26659

• mlavanga/nonlinear_signals_analysis

  A short introduction how to recognise a nonlinear signals and extract the associated summary statistics

  Language:Jupyter Notebook41

• ivy-llc/ivy

  Convert Machine Learning Code Between Frameworks

  Language:Python14k5.7k

• FluidityProject/fluidity

  Fluidity

  Language:Fortran367117

• Wo0kie3/Simulation-of-the-plane-boarding-strategies

  Me and my friend, Artificial life project

  Language:Python52

• benalexkeen/Introduction-to-linear-programming

  Introduction to Linear Programming with Python

  Language:Jupyter Notebook16983

• evgps/a3c_trading

  Trading with recurrent actor-critic reinforcement learning

  Language:Jupyter Notebook427126

• PacktPublishing/Hands-On-Genetic-Algorithms-with-Python

  Hands-On Genetic Algorithms with Python, Published by Packt

  Language:Python269140

• tomduck/electrostatics

  Electrostatic field line and equipotential contour diagrams

  Language:Python6719

• itdxer/neupy

  NeuPy is a Tensorflow based python library for prototyping and building neural networks

  Language:Python738160

• nickmccullum/algorithmic-trading-python

  The repository for freeCodeCamp's YouTube course, Algorithmic Trading in Python

  Language:Jupyter Notebook2.5k2.5k

• john-bradshaw/GNN

  Graph neural networks

  Language:Python194

• tkipf/gcn

  Implementation of Graph Convolutional Networks in TensorFlow

  Language:Python7.2k2k

• dean0x7d/pybinding

  Scientific Python package for tight-binding calculations in solid state physics

  Language:C++19069