TUM-ITR / koopcoreLinks
Koopman Kernels for Learning Dynamical Systems from Trajectory Data
☆27Updated last year
Alternatives and similar repositories for koopcore
Users that are interested in koopcore are comparing it to the libraries listed below
Sorting:
- A Python package to learn the Koopman operator.☆57Updated 6 months ago
- A framework for neural network control of dynamical systems over graphs.☆58Updated 2 years ago
- Implementation of the work Variational multiple shooting for Bayesian ODEs with Gaussian processes☆12Updated 2 years ago
- Data-driven dynamical systems toolbox.☆74Updated 3 weeks ago
- A PyTorch library for all things nonlinear control and reinforcement learning.☆46Updated 3 years ago
- AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.☆73Updated last year
- DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control☆23Updated 4 years ago
- Koopman operator identification library in Python, compatible with `scikit-learn`☆75Updated 3 weeks ago
- A Neural Network Approach for Real-Time High-Dimensional Optimal Control☆26Updated 2 years ago
- IIB Master's Project: Deep Learning for Koopman Optimal Predictive Control☆45Updated 4 years ago
- ☆29Updated 2 years ago
- Neural Laplace Control for Continuous-time Delayed Systems - an offline RL method combining Neural Laplace dynamics model and MPC planner…☆12Updated 2 years ago
- ☆15Updated 4 years ago
- Code for "Learning Control-Oriented Dynamical Structure from Data" by Spencer M. Richards, Jean-Jacques Slotine, Navid Azizan, and Marco …☆15Updated last year
- ☆19Updated 5 years ago
- Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…☆40Updated 6 years ago
- Uncertainty sets for nonlinear dynamical systems☆10Updated 4 years ago
- [ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control☆90Updated 4 years ago
- Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling☆48Updated 3 years ago
- By introducing a differentiable contact model, DiffCoSim extends the applicability of Lagrangian/Hamiltonian-inspired neural networks to …☆36Updated 2 years ago
- ☆20Updated last year
- ☆43Updated 3 years ago
- A general-purpose Python package for Koopman theory using deep learning.☆104Updated 3 months ago
- Learning unknown ODE models with Gaussian processes☆26Updated 6 years ago
- Consistent Koopman Autoencoders☆74Updated 2 years ago
- Online variational GPs☆34Updated 2 years ago
- "dynoNet: A neural network architecture for learning dynamical systems" by Marco Forgione and Dario Piga☆44Updated last year
- ☆40Updated last year
- Official implementation for our paper "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"☆19Updated 2 years ago
- Nonparametric Differential Equation Modeling☆53Updated last year