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awesome-sustainable-technology

Gathering state-of-the-art open source technology preserving global energy supply and vital natural resources

data-driven-advection

generativedenseunet

An RBF based artificial neural network used for the purpose of semantic image segmentation. The backbone of this model is a DenseUNet. The head of the convolutional neural network is an RBF layer. Inference can be done using Bayes Theorem, or Softmax.

geo-gcn

The official implementation of the SGCN architecture.

gnn_review

GNN综述阅读报告

gnns-for-nlp

Tutorial: Graph Neural Networks for Natural Language Processing at EMNLP 2019 and CODS-COMAD 2020

impedance.py

A Python package for working with electrochemical impedance data

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

l2d-learning-to-discretize

source code of learning to discretize solving 1d scalar conservation laws via deep reinforcement learning

locogan

PyTorch implmentation of LocoGAN: https://arxiv.org/abs/2002.07897

mim_boundary_condition

navier-stokes-numerical-solution-using-fdm-fvm-lbm-solver-python-scripting

Python script for Linear, Non-Linear Convection, Burger’s & Poisson Equation in 1D & 2D, 1D Diffusion Equation using Standard Wall Function, 2D Heat Conduction Convection equation with Dirichlet & Neumann BC, full Navier-Stokes Equation coupled with Poisson equation for Cavity and Channel flow in 2D using Finite Difference Method & Finite Volume Me

neural_differential_equations

This is the code for "Neural DIfferential Equations" By Siraj Raval on Youtube

neuralsolvers

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

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 equilibri

openpnm

A Python package for performing pore network modeling of porous media

pemfc-core

physics-informed-deep-learning-solid-and-fluid-mechanics

Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems

pinns

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

pore3d

porepy

Python Simulation Tool for Fractured and Deformable Porous Media

pretrain-gnns

Strategies for Pre-training Graph Neural Networks

python-graphs

A static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.

pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

pytorchtutorial

Examplary code for NN, MFNN, DynNet, PINNs and CNN

rmt_theory_applications

some important properties on random matrix theory and its applications in multiple areas

scientific_computing

stochastic_seismic_waveform_inversion

Official Implementation of "Stochastic seismic waveform inversion using generative adversarial networks as a geological prior"

swin-transformer

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

t-gcn

Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method