Liu-Hairui

generative-models

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

GAN

Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

efficientnet

Implementation of EfficientNet model. Keras and TensorFlow Keras.

ConvLSTM-Computer-Vision-for-Structural-Health-Monitoring-SHM-and-NonDestructive-Testing-NDT

Application of LSTM network for Structural Health Monitoring & Non-Destructive Testing

CT-RED_CNN_tensorflow

Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

En-DeepONet

Repository for sharing code and data assocaited with En-DeepONet architecture

SimpleFWI

Simple Matlab code for testing optimization algorithms on seismic inverse problems.

FM

Fast Marching (FM) method implementation in Matlab and C++

hp-VPINNs

hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations

EikoNet

Machine learning formulation for the Factored Eikonal Equation

pySART

Tomographic reconstruction using the Simultaneous Algebraic Reconstruction Technique (SART) implemented in python.

sciann-applications

A place to share problems solved with SciANN

pykonal

Travel-time calculator based on the fast-marching method solution to the Eikonal equation.

PINNeikonal

deepxde

A library for scientific machine learning and physics-informed learning

PINN-Voltammetry-Best-Practices

homework

Physics-informed-DeepONets

deeponet

Learning nonlinear operators via DeepONet

models

neuraloperator

Learning in infinite dimension with neural operators.

PINNpapers

Must-read Papers on Physics-Informed Neural Networks.

NeuralPDE.jl

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

PINNs

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

PINNs-TF2.0

TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).

load-forecasting-algorithms

使用多种算法（线性回归、随机森林、支持向量机、BP神经网络、GRU、LSTM）进行电力系统负荷预测/电力预测。通过一个简单的例子。A variety of algorithms (linear regression, random forest, support vector machine, BP neural network, GRU, LSTM) are used for power system load forecasting / power forecasting.

PINN-Helmholtz-solver-adaptive-sine

py-pde

Python package for solving partial differential equations using finite differences.

PDEsByNNs

This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means of neural networks using TensorFlow.

09_turbofan_rul

Remaining useful life prediction for turbofan engine data (C-MAPSS)