sjtu-rui's Stars
jackfrued/Python-100-Days
Python - 100天从新手到大师
pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
microsoft/ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
binary-husky/gpt_academic
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。
INTERMT/Awesome-PyTorch-Chinese
【干货】史上最全的PyTorch学习资源汇总
swaroopch/byte-of-python
Beginners book on Python - start here if you don't know programming
weijie-chen/Linear-Algebra-With-Python
Lecture Notes for Linear Algebra Featuring Python. This series of lecture notes will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative skillsets. Suitable for statistician/econometrician, quantitative analysts, data scientists and etc. to quickly refresh the linear algebra with the assistance of Python computation and visualization.
openai/maddpg
Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
ivankokan/Excel2LaTeX
The Excel add-in for creating LaTeX tables
locuslab/mpc.pytorch
A fast and differentiable model predictive control (MPC) solver for PyTorch.
Shunichi09/PythonLinearNonlinearControl
PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.
nicklashansen/tdmpc
Code for "Temporal Difference Learning for Model Predictive Control"
OpenOCL/OpenOCL
Open Optimal Control Library for Matlab. Trajectory Optimization and non-linear Model Predictive Control (MPC) toolbox.
fendouai/pytorch1.0-cn
PyTorch 1.0 官方文档 中文版，欢迎关注微信公众号：磐创AI
MMehrez/MPC-and-MHE-implementation-in-MATLAB-using-Casadi
This is a workshop on implementing model predictive control (MPC) and moving horizon estimation (MHE) on Matlab. The implementation is based on the Casadi Package which is used for numerical optimization. A non-holonomic mobile robot is used as a system for the implementation. The workshop video recording can be found here https://www.youtube.com/playlist?list=PLK8squHT_Uzej3UCUHjtOtm5X7pMFSgAL ... Casadi can be downloaded here https://web.casadi.org/
lucasrm25/Gaussian-Process-based-Model-Predictive-Control
Project for the course "Statistical Learning and Stochastic Control" at University of Stuttgart
tomcattiger1230/CasADi_MPC_MHE_Python
This repository is an implementation of the work from Mohamed W. Mehrez. I convert the original code in MATLAB to the Python
arbabiha/KoopmanMPC_for_flowcontrol
A data-driven framework for control of nonlinear flows with Koopman Model Predictive Control
Jonas-Nicodemus/PINNs-based-MPC
We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approximate (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not designed to handle variable control actions or variable initial values. We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. The high-dimensional input space is subsequently reduced via a sampling strategy and a zero-hold assumption. This strategy enables the controller design based on a PINN as an approximation of the underlying system dynamics. The additional benefit is that the sensitivities are easily computed via automatic differentiation, thus leading to efficient gradient-based algorithms. Finally, we present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system, a multi-link manipulator.
befelix/safe-exploration
Safe Exploration with MPC and Gaussian process models
DangFengying/RL-based-event-triggered-MPC
MilanKorda/KoopmanMPC
i-abr/mpc-koopman
Model-based Control using Koopman Operators
pnnl/deps_arXiv2020
Differentiable predictive control (DPC) policy optimization examples.
SNU-EPEL/Integration-of-Reinforcement-Learning-and-Model-Predictive-Control-to-Optimize-Semi-batch-Bioreactor
joelpaulson/ADCHEM_ML_MPC_Workshop_2021
Code associated with the ADCHEM 2021 Workshop on Machine Learning and Model Predictive Control
adbonzanini/LB-Multi-Stage-NMPC
Learning-based multi-stage NMPC algorithm with guarantees on feasibility using robust control invariant sets
dinesh-krishnamoorthy/Sensitivity-DataAugmentation
Sensitivity-based Data Augmentation framework for optimal control problems
detu/licq-path-following
implementation of path-following (NLP sensitivity) algorithm for LICQ case
brittanh/masters-project
Brittany Hall's master project Autumn 2017