Alternatives and similar repositories for pykoop

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

• EthanJamesLew / AutoKoopman
  AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
  ☆73Updated last year
• sklus / d3s
  Data-driven dynamical systems toolbox.
  ☆74Updated 3 weeks ago
• GaloisInc / dlkoopman
  A general-purpose Python package for Koopman theory using deep learning.
  ☆103Updated 3 months ago
• urban-fasel / SEIR_SINDY_MPC
  ☆43Updated 3 years ago
• ContiPaolo / EKF-SINDy
  Empowering extended Kalman filter (EKF) with Sparse Identification of Nonlinear Dynamics (SINDy)
  ☆55Updated last month
• Machine-Learning-Dynamical-Systems / kooplearn
  A Python package to learn the Koopman operator.
  ☆57Updated 6 months ago
• eurika-kaiser / KRONIC
  Koopman Reduced-Order Nonlinear Identification and Control
  ☆90Updated 5 years ago
• learning-and-control / core
  Python simulation and hardware library for learning and control
  ☆20Updated 2 years ago
• forgi86 / dynonet
  "dynoNet: A neural network architecture for learning dynamical systems" by Marco Forgione and Dario Piga
  ☆44Updated last year
• DiffEqML / torchcontrol
  A PyTorch library for all things nonlinear control and reinforcement learning.
  ☆46Updated 3 years ago
• xl402 / deep-koopman
  IIB Master's Project: Deep Learning for Koopman Optimal Predictive Control
  ☆45Updated 4 years ago
• dynamicslab / modified-SINDy
  Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…
  ☆55Updated 3 years ago
• Cafolkes / keedmd
  Repository for construction of Koopman eigenfunctions for unknown dynamical systems and identification of a lifted state-space model usin…
  ☆28Updated 2 years ago
• TUM-ITR / koopcore
  Koopman Kernels for Learning Dynamical Systems from Trajectory Data
  ☆27Updated last year
• haozhg / oml
  AI4Science: Efficient data-driven Online Model Learning (OML) / system identification and control
  ☆30Updated 2 years ago
• donken / NeuralOC
  A Neural Network Approach for Real-Time High-Dimensional Optimal Control
  ☆26Updated 2 years ago
• i-abr / mpc-koopman
  Model-based Control using Koopman Operators
  ☆51Updated 4 years ago
• fedebotu / optimal-control-exercises
  Exercise solutions for Daniel Liberzon's "Calculus of Variations and Optimal Control Theory: A Concise Introduction" book and other optim…
  ☆24Updated 3 years ago
• asikist / nnc
  A framework for neural network control of dynamical systems over graphs.
  ☆58Updated 2 years ago
• d-biswa / Symplectic-ODENet
  ☆15Updated 4 years ago
• PredictiveIntelligenceLab / GP-NODEs
  ☆29Updated 2 years ago
• decargroup / closed_loop_koopman
  Companion code for Closed-Loop Koopman Operator Approximation
  ☆16Updated last year
• manschuer / SRGP
  stochastic recursive Gaussian Process (SRGP)
  ☆20Updated 4 years ago
• MaartenSchoukens / deepSI
  Dynamical System Identification using python incorporating numerous powerful deep learning methods. (deepSI = deep System Identification)
  ☆57Updated last month
• IlyaOrson / Control-NeuralODE
  Neural ODEs as Feedback Policies for Nonlinear Optimal Control (IFAC 2023) https://doi.org/10.1016/j.ifacol.2023.10.1248
  ☆16Updated last year
• bemporad / jax-sysid
  A Python package for linear subspace identification, nonlinear system identification, and nonlinear regression using Jax
  ☆34Updated last month
• wuwushrek / physics_constrained_nn
  Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
  ☆48Updated 3 years ago
• loiseaujc / SINDy
  ☆41Updated 7 years ago
• thaipduong / SE3HamDL
  Code for our RSS'21 paper: "Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control"
  ☆41Updated last year
• coursekevin / svise
  Accompanying code for "State Estimation of a Physical System without Governing Equations"
  ☆86Updated 10 months ago