Alternatives and similar repositories for CompositionalKoopmanOperators

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

• 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
• xl402 / deep-koopman
  IIB Master's Project: Deep Learning for Koopman Optimal Predictive Control
  ☆48Updated 4 years ago
• vmartinezalvarez / DyNODE
  DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control
  ☆23Updated 5 years ago
• wuwushrek / physics_constrained_nn
  Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
  ☆52Updated 3 years ago
• locuslab / stable_dynamics
  Companion code to "Learning Stable Deep Dynamics Models" (Manek and Kolter, 2019)
  ☆33Updated 5 years ago
• asikist / nnc
  A framework for neural network control of dynamical systems over graphs.
  ☆56Updated 3 years ago
• Physics-aware-AI / Symplectic-ODENet
  ☆47Updated 4 years ago
• Cafolkes / keedmd
  Repository for construction of Koopman eigenfunctions for unknown dynamical systems and identification of a lifted state-space model usin…
  ☆27Updated 2 years ago
• locuslab / robust-nn-control
  Enforcing robust control guarantees within neural network policies
  ☆55Updated 4 years ago
• TUM-ITR / koopcore
  Koopman Kernels for Learning Dynamical Systems from Trajectory Data
  ☆31Updated last year
• i-abr / mpc-koopman
  Model-based Control using Koopman Operators
  ☆52Updated 5 years ago
• aravindsrinivas / neural-mpc
  ☆74Updated 5 years ago
• donken / NeuralOC
  A Neural Network Approach for Real-Time High-Dimensional Optimal Control
  ☆27Updated 3 years ago
• YaChienChang / Neural-Lyapunov-Control
  Learning Lyapunov functions and control policies of nonlinear dynamical systems
  ☆137Updated 4 years ago
• dgedon / DeepSSM_SysID
  Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.
  ☆96Updated 3 years ago
• NoemieJaquier / GaBOtorch
  This repository contains the source code to perform Geometry-aware Bayesian Optimization (GaBO) on Riemannian manifolds.
  ☆53Updated 3 years ago
• tony23545 / DeepKoopman
  Use deep learning to learn Koopman operator and LQR for optimal control
  ☆16Updated 5 years ago
• JoeMWatson / input-inference-for-control
  Input Inference for Control (i2c), a control-as-inference framework for optimal control
  ☆25Updated 2 years ago
• homangab / gradcem
  Code for the paper Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization
  ☆68Updated 5 years ago
• DiffEqML / torchcontrol
  A PyTorch library for all things nonlinear control and reinforcement learning.
  ☆47Updated 3 years ago
• young-j-park / 18-NeurIPS-APIAE
  ☆21Updated 6 years ago
• i-abr / active-learning-koopman
  ☆24Updated 4 years ago
• wanxinjin / Pontryagin-Differentiable-Programming
  A unified end-to-end learning and control framework that is able to learn a (neural) control objective function, dynamics equation, contr…
  ☆178Updated last year
• fxia22 / gn.pytorch
  ☆52Updated 6 years ago
• cagatayyildiz / oderl
  Experiment code for "Continuous-Time Model-Based Reinforcement Learning"
  ☆54Updated last year
• DesmondZhong / Lagrangian_caVAE
  Official implementation for our paper "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"
  ☆19Updated 3 years ago
• erichson / koopmanAE
  Consistent Koopman Autoencoders
  ☆74Updated 2 years ago
• Stanford-ILIAD / active-preference-based-gpr
  Companion code for RSS 2020 paper: "Active Preference-Based Gaussian Process Regression for Reward Learning"
  ☆39Updated last year
• SimonRennotte / Data-Efficient-Reinforcement-Learning-with-Probabilistic-Model-Predictive-Control
  Unofficial Implementation of the paper "Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control", applied to gy…
  ☆145Updated 2 years ago
• yuan-yin / APHYNITY
  Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
  ☆47Updated last year