Alternatives and similar repositories for koopcore

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

• asikist / nnc
  A framework for neural network control of dynamical systems over graphs.
  ☆57Updated 2 years ago
• Machine-Learning-Dynamical-Systems / kooplearn
  A Python package to learn the Koopman operator.
  ☆61Updated 9 months ago
• vmartinezalvarez / DyNODE
  DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control
  ☆23Updated 5 years ago
• xl402 / deep-koopman
  IIB Master's Project: Deep Learning for Koopman Optimal Predictive Control
  ☆48Updated 4 years ago
• wuwushrek / physics_constrained_nn
  Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
  ☆52Updated 3 years ago
• YunzhuLi / CompositionalKoopmanOperators
  [ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control
  ☆90Updated 4 years ago
• DiffEqML / torchcontrol
  A PyTorch library for all things nonlinear control and reinforcement learning.
  ☆47Updated 3 years ago
• decargroup / pykoop
  Koopman operator identification library in Python, compatible with `scikit-learn`
  ☆84Updated 2 months ago
• PredictiveIntelligenceLab / GP-NODEs
  ☆29Updated 2 years ago
• NoemieJaquier / GaBOtorch
  This repository contains the source code to perform Geometry-aware Bayesian Optimization (GaBO) on Riemannian manifolds.
  ☆53Updated 3 years ago
• sklus / d3s
  Data-driven dynamical systems toolbox.
  ☆75Updated last month
• ivandariojr / LyapunovLearning
  ☆43Updated 2 years ago
• dgedon / DeepSSM_SysID
  Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.
  ☆96Updated 3 years ago
• donken / NeuralOC
  A Neural Network Approach for Real-Time High-Dimensional Optimal Control
  ☆27Updated 3 years ago
• yuan-yin / APHYNITY
  Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
  ☆47Updated last year
• sisl / variational_koopman
  Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…
  ☆42Updated 6 years ago
• d-biswa / Symplectic-ODENet
  ☆15Updated 4 years ago
• DesmondZhong / Lagrangian_caVAE
  Official implementation for our paper "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"
  ☆19Updated 3 years ago
• Physics-aware-AI / DiffCoSim
  By introducing a differentiable contact model, DiffCoSim extends the applicability of Lagrangian/Hamiltonian-inspired neural networks to …
  ☆36Updated 2 years ago
• EthanJamesLew / AutoKoopman
  AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
  ☆78Updated last year
• hegdepashupati / gaussian-process-odes
  Implementation of the work Variational multiple shooting for Bayesian ODEs with Gaussian processes
  ☆13Updated 3 years ago
• clegaard / deep_learning_for_dynamical_systems
  ☆88Updated 2 years ago
• cagatayyildiz / npde
  Nonparametric Differential Equation Modeling
  ☆54Updated last year
• GaloisInc / dlkoopman
  A general-purpose Python package for Koopman theory using deep learning.
  ☆108Updated 7 months ago
• cagatayyildiz / npode
  Learning unknown ODE models with Gaussian processes
  ☆26Updated 7 years ago
• Physics-aware-AI / Symplectic-ODENet
  ☆47Updated 4 years ago
• Bigpig4396 / Incremental-Gaussian-Process-Regression-IGPR
  ☆16Updated 3 years ago
• JoeMWatson / input-inference-for-control
  Input Inference for Control (i2c), a control-as-inference framework for optimal control
  ☆25Updated 2 years ago
• wjmaddox / online_gp
  Code repo for "Kernel Interpolation for Scalable Online Gaussian Processes"
  ☆64Updated 4 years ago
• wjmaddox / online_vargp
  Online variational GPs
  ☆37Updated 2 years ago