ViniMelloeMuller's Stars
guanyingc/latex_paper_writing_tips
Tips for Writing a Research Paper using LaTeX
starship/starship
☄🌌️ The minimal, blazing-fast, and infinitely customizable prompt for any shell!
bluesky-social/social-app
The Bluesky Social application for Web, iOS, and Android
garrettj403/SciencePlots
Matplotlib styles for scientific plotting
astral-sh/ruff-lsp
A Language Server Protocol implementation for Ruff.
neovide/neovide
No Nonsense Neovim Client in Rust
junegunn/vim-plug
:hibiscus: Minimalist Vim Plugin Manager
sharkdp/bat
A cat(1) clone with wings.
dandavison/delta
A syntax-highlighting pager for git, diff, grep, and blame output
HigherOrderCO/Bend
A massively parallel, high-level programming language
locuslab/mpc.pytorch
A fast and differentiable model predictive control (MPC) solver for PyTorch.
junegunn/fzf
:cherry_blossom: A command-line fuzzy finder
aresio/simpful
A friendly python library for fuzzy logic reasoning
joaomlourenco/biblatex-cse
BibLaTeX: support for Council of Science Editors style
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.
tensorforce/tensorforce
Tensorforce: a TensorFlow library for applied reinforcement learning
vojtamolda/reinforcement-learning-an-introduction
Solutions to exercises in Reinforcement Learning: An Introduction (2nd Edition).
doandongnguyen/FQL-Fuzzy-Q-Learning
Fuzzy Q-Learning Algorithm
seyedsaeidmasoumzadeh/Fuzzy-Q-Learning
A Python implementation of Fuzzy Q-Learning (FQL) for any controllers with continues states
aresio/fst-pso
A settings-free global optimization method based on PSO and fuzzy logic
UniversidadPolitecnicaAguascalientes/UPAFuzzySystems
UPAFuzzySystems library that allows defining Fuzzy Inference Systems for different applications with continuous and discrete universes, it also deploys structures for the simulation of fuzzy control with transfer functions and state space models.
ahmedfgad/GeneticAlgorithmPython
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
d2l-ai/d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
dynamicslab/pysindy
A package for the sparse identification of nonlinear dynamical systems from data
trevorstephens/gplearn
Genetic Programming in Python, with a scikit-learn inspired API
dynamicslab/pykoopman
A package for computing data-driven approximations to the Koopman operator.
Rafael1s/Deep-Reinforcement-Learning-Algorithms
32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
ehsanhaghighat/sciann
Deep learning for Engineers - Physics Informed Deep Learning
adriangb/scikeras
Scikit-Learn API wrapper for Keras.
python-control/python-control
The Python Control Systems Library is a Python module that implements basic operations for analysis and design of feedback control systems.