ZiFeng Xu's repositories
SGDA
Code for Semi-supervised Domain Adaptation in Graph Transfer Learning
opendigitaltwins-building
WillowTwin open digital twin definition language (DTDL) ontology for buildings and real estate
surmise
A python package for surrogate models that interface with calibration and other tools
PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
Vibration-Based-Fault-Diagnosis-with-Low-Delay
Python codes “Jupyter notebooks” for the paper entitled "A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay, IEEE Trans. on Instrumentation and Measurement, Aug. 2022. Techniques used: Wavelet Packet Transform (WPT) & Fast Fourier Transform (FFT). Application: vibration-based fault diagnosis.
pinn_wind_bearing
Python scripts for wind turbine main bearing fatigue life estimation with physics-informed neural networks
ar-pde-cnn
Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs
ML_gzh
常用机器学习算法的简单手写实现，帮助更好理解算法
stockwell
Stockwell transform for Python
LSTM
基于LSTM神经网络的时间序列预测
leetcode-algorithm
分类整理leetcode算法题解，代码语言采用c++与python实现
TimeSeriesForecasting-DeepLearning
An experiemtal review on deep learning architectures for time series forecasting
Dive-into-DL-TensorFlow2.0
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的认可
transformer-time-series-prediction
proof of concept for a transformer-based time series prediction model
diagnose_fault_by_vibration
毕设研究课题：根据轴承的振动序列数据来诊断轴承故障。
sa
信号分析及数据可视化软件
Time-Series-Demand-Forecasting
Time-series demand forecasting is constructed by using LSTM, GRU, LSTM with seq2seq architecture, and prophet models.
DeepLearningForTSF
深度学习以进行时间序列预测
DigitalTwin_Tutorial
Digital twin for structural dynamics applications
grace_jiubao
shanghai university for myDoctor of Philosophy
PyQt5-Learning
this is the store for learning PyQt5 and some code、context