Pinned Repositories
deep-learning-book
《Deep Learning》《深度学习》 by Ian Goodfellow, Yoshua Bengio and Aaron Courville
devtraining-needit-istanbul
This repository is used by the developer site training content, istanbul release. It is used for the Build the NeedIt App, Scripting in ServiceNow, Application Security, Importing Data, and Automating Application Logic courses.
devtraining-needit-jakarta
This repository is used by the developer site training content, jakarta release. It is used for the Build the NeedIt App, Scripting in ServiceNow, Application Security, Importing Data, and Automating Application Logic courses.
face_clustering
geneticAlgorithm
tuto and experiment with genetic algorithms.
git_learning
study
gmaps
Google maps for Jupyter notebooks
Google-Interview-prep
Just looking at basic python functiosn and scripts to prep for google interview
Heatmap_of_cites_in_VT
Heatmap of the top 100 cites in Vermont
interview
Everything you need to prepare for your technical interview
kewangchen's Repositories
kewangchen/aa
Anderson Acceleration_Python_Type_1_and_type_2
kewangchen/AADL
Anderson Acceleration for Deep Learning
kewangchen/cosmo-python
Python interface for COSMO.jl convex optimisation solver.
kewangchen/Covid_19_Insights
Determine the features that best predict Covid-19 infection/case & death rates based on data from counties across the US and allow users to predict case & death rates from county feature input data.
kewangchen/cvx_short_course
Materials for a short course on convex optimization.
kewangchen/data
Data Sets for Machine Learning Practice
kewangchen/ebook
收集的电子书资源
kewangchen/Fast-Robust-ICP
kewangchen/FBPINNs
Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)
kewangchen/fenics-tutorial
Source files and published documents for the FEniCS tutorial.
kewangchen/FixedPointAcceleration.jl
Fixed Point Acceleration for Julia
kewangchen/FractionalDiffEq.jl
Solve Fractional Differential Equations using high performance numerical methods
kewangchen/harmonic-oscillator-pinn
Code accompanying my blog post: So, what is a physics-informed neural network?
kewangchen/jacobian_free_backprop
Implicit networks can be trained efficiently and simply by using Jacobian-free Backprop (JFB).
kewangchen/Learning-Scientific_Machine_Learning_Residual_Based_Attention_PINNs_DeepONets
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
kewangchen/liblbfgs
libLBFGS: a library of Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS)
kewangchen/My-awesome-PINN-papers
kewangchen/NAGPythonExamples
Examples and demos showing how to call functions from the NAG Library for Python
kewangchen/Natural-Gradient-PINNs-ICML23
This is the repository for the code used in the ICML23 paper called "Achieving High Accuracy with PINNs via Energy Natural Gradient Descent"
kewangchen/Neural-PDE-Solver
kewangchen/neural-scs
Neural Fixed-Point Acceleration for Convex Optimization
kewangchen/NeuralPDE.jl
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
kewangchen/NNFS-book-with-Implementation
Book and code where describe each and every topic of neural network from scratch.
kewangchen/Physics-Informed-Neural-Networks
Investigating PINNs
kewangchen/PMNN
kewangchen/PreAA
preAA++ is a header-only C++ implementation that offers the (preconditioned) Anderson acceleration algorithm (pAA) for fixed-point iteration problems. Additionally, it provides a versatile framework for the preconditioned Anderson acceleration algorithm.
kewangchen/precice
A coupling library for partitioned multi-physics simulations, including, but not restricted to fluid-structure interaction and conjugate heat transfer simulations.
kewangchen/pumpkin-book
《机器学习》（西瓜书）公式详解
kewangchen/sundials
Official development repository for SUNDIALS - a SUite of Nonlinear and DIfferential/ALgebraic equation Solvers. Pull requests are welcome for bug fixes and minor changes.
kewangchen/THU-Mathematics-Books
清华数学系部分课程教材及参考资料，自用，侵删