Alternatives and similar repositories for PIDAO

Users that are interested in PIDAO are comparing it to the libraries listed below

• wuwushrek / physics_constrained_nn
  Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
  ☆52Updated 3 years ago
• Koopman-Laboratory / KoopmanLab
  A library for Koopman Neural Operator with Pytorch.
  ☆308Updated last year
• Jonas-Nicodemus / PINNs-based-MPC
  We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-infor…
  ☆127Updated 2 years ago
• TomJZ / K-NODE
  Knowledge-based learning of nonlinear dynamics and chaos
  ☆16Updated 2 years ago
• Denis-Khimin / OptimalControlPDEAutoDiff
  Optimal Control with PDEs solved by a Differentiable Solver
  ☆12Updated last year
• CarlDegio / deepkoopman
  deepkoopman的实现
  ☆12Updated 2 years ago
• GaloisInc / dlkoopman
  A general-purpose Python package for Koopman theory using deep learning.
  ☆110Updated last month
• Jianxun-Wang / PIMBRL
  Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control
  ☆56Updated 3 years ago
• milutter / deep_lagrangian_networks
  Open-source implementation of Deep Lagrangian Networks (DeLaN)
  ☆127Updated 11 months ago
• MLDS-NUS / KoopmanDL
  Learning Koopman operator by EDMD with trainable dictionary
  ☆27Updated 3 years ago
• HaojieSHI98 / DeepKoopmanWithControl
  Soure code for Deep Koopman with Control
  ☆93Updated 3 years ago
• gaoliyao / BayesianSindyAutoencoder
  Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. Proceedings of the Royal S…
  ☆10Updated last year
• EthanJamesLew / AutoKoopman
  AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
  ☆79Updated last year
• luckystarufo / DMD_for_Human_motion
  ☆36Updated 7 years ago
• MarcusHA94 / structural-dynamics-pinns
  Research/development of physics-informed neural networks for dynamic systems
  ☆31Updated 11 months ago
• AI4Science-WestlakeU / diffphycon
  [NeurIPS2024] DiffPhyCon uses generative models to control complex physical systems
  ☆44Updated this week
• asikist / nnc
  A framework for neural network control of dynamical systems over graphs.
  ☆56Updated 3 years ago
• adi3e08 / Physics_Informed_Model_Based_RL
  We learn the dynamics model of a robot using a physics-informed neural network and use it to train a model-based RL algorithm.
  ☆45Updated last year
• jhbastek / PhysicsInformedDiffusionModels
  Implementation of Physics-Informed Diffusion Models (ICLR 2025)
  ☆148Updated 8 months ago
• sisl / variational_koopman
  Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…
  ☆43Updated 6 years ago
• xl402 / deep-koopman
  IIB Master's Project: Deep Learning for Koopman Optimal Predictive Control
  ☆49Updated 4 years ago
• d-biswa / Symplectic-ODENet
  ☆15Updated 4 years ago
• isds-neu / PiSL
  A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.
  ☆27Updated 4 years ago
• xuangu-fang / Gaussian-Process-Slover-for-High-Freq-PDE
  Solving High Frequency and Multi-Scale PDEs with Gaussian Processes (ICLR 2024)
  ☆25Updated last year
• decargroup / pykoop
  Koopman operator identification library in Python, compatible with `scikit-learn`
  ☆98Updated last month
• menggedu / DISCOVER
  Deep identification of symbolic open-form PDEs via enhanced reinforcement-learning
  ☆39Updated 10 months ago
• weishiyan / Physics-Informed-Reinforcement-Learning
  ☆10Updated 4 years ago
• paulpuren / PhyCRNet
  Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs
  ☆30Updated 3 years ago
• cvlab-epfl / CFD_Mesh_Parameterization
  Codes for paper "Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning", published on AIAA Aviation F…
  ☆19Updated last year
• fish-gym / gym-fish
  ☆50Updated last year