Pinned Repositories
100-Days-Of-ML-Code
100-Days-Of-ML-Code中文版
AFiD
A highly parallel application for Rayleigh-Benard and Taylor-Couette flows
CFDPython
A sequence of Jupyter notebooks featuring the "12 Steps to Navier-Stokes" http://lorenabarba.com/
cnn-surrogate
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
Cylinder2DFlowControlDRL
code for performing active flow control of the 2D Karman street using Deep Reinforcement Learning
Cylinder2DFlowControlDRLParallel
Parallelizing DRL for Active Flow control
deep_flow_control
Source code for "Deep Dynamical Modeling and Control of Unsteady Fluid Flows" from NIPS 2018
DeepHPMs
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
deepMKL
Deep Multiple Kernel Learning by Span Bound
DeepReinforcementLearningInAction
Code from the Deep Reinforcement Learning in Action book from Manning, Inc
QinghuaJIANG's Repositories
QinghuaJIANG/100-Days-Of-ML-Code
100-Days-Of-ML-Code中文版
QinghuaJIANG/Cylinder2DFlowControlDRL
code for performing active flow control of the 2D Karman street using Deep Reinforcement Learning
QinghuaJIANG/Cylinder2DFlowControlDRLParallel
Parallelizing DRL for Active Flow control
QinghuaJIANG/DeepHPMs
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
QinghuaJIANG/DeepReinforcementLearningInAction
Code from the Deep Reinforcement Learning in Action book from Manning, Inc
QinghuaJIANG/DeepVIV
Deep Learning of Vortex Induced Vibrations
QinghuaJIANG/drl_shape_optimization
Deep reinforcement learning to perform shape optimization
QinghuaJIANG/ENM531
QinghuaJIANG/FBSNNs
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations
QinghuaJIANG/GPTutorial
A hands-on tutorial on supervised learning with Gaussian processes
QinghuaJIANG/GradientPathologiesPINNs
QinghuaJIANG/HFM
Hidden Fluid Mechanics
QinghuaJIANG/HWStatusCodeAutoQuery
华为招聘状态码全天候在线自动查询工具
QinghuaJIANG/LabelFree-DNN-Surrogate
Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning
QinghuaJIANG/Locally-Adaptive-Activation-Functions-Neural-Networks-
These are the set of 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)".
QinghuaJIANG/MrCAE
a multiresolution convolutional autoencoder architecture
QinghuaJIANG/MultistepNNs
Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems
QinghuaJIANG/OpenFSI
A highly efficient and portable fluid-structure simulation package based on immersed-boundary method
QinghuaJIANG/PDEBench
PDEBench: An Extensive Benchmark for Scientific Machine Learning
QinghuaJIANG/PhyCRNet
Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
QinghuaJIANG/phygeonet
PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain
QinghuaJIANG/Physics-constrained-Bayesian-deep-learning
Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data
QinghuaJIANG/pumpkin-book
《机器学习》（西瓜书）公式推导解析，在线阅读地址：https://datawhalechina.github.io/pumpkin-book
QinghuaJIANG/pySINDy
SINDy (Sparse Identification of Nonlinear Dynamics) algorithms
QinghuaJIANG/pysindy-1
A package for the sparse identification of nonlinear dynamical systems from data
QinghuaJIANG/RLFluidControl
reinforcement learning for active fluid control
QinghuaJIANG/scientific-visualization-book
An open access book on scientific visualization using python and matplotlib
QinghuaJIANG/TutorialGP
Tutorial on Gaussian Processes
QinghuaJIANG/WZU-machine-learning-course
温州大学《机器学习》课程资料（代码、课件等）
QinghuaJIANG/XPINNs
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations