yriyazi / Koopman-Operator-and-Deep-Neural-Networks-ISAV2023Links
In this work, we present a novel approach that combines the power of Koopman operators and deep neural networks to generate a linear representation of the Duffing oscillator. This approach enables effective parameter estimation and accurate prediction of the oscillator's future behavior.
☆10Updated 3 weeks ago
Alternatives and similar repositories for Koopman-Operator-and-Deep-Neural-Networks-ISAV2023
Users that are interested in Koopman-Operator-and-Deep-Neural-Networks-ISAV2023 are comparing it to the libraries listed below
Sorting:
- IIB Master's Project: Deep Learning for Koopman Optimal Predictive Control☆50Updated 5 years ago
- AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.☆82Updated last year
- Consistent Koopman Autoencoders☆75Updated 2 years ago
- AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)☆44Updated 3 years ago
- ☆89Updated 2 years ago
- Data-driven dynamical systems toolbox.☆80Updated 2 months ago
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆30Updated 3 years ago
- ☆45Updated 4 years ago
- ☆15Updated 5 years ago
- A framework for neural network control of dynamical systems over graphs.☆56Updated 3 years ago
- A general-purpose Python package for Koopman theory using deep learning.☆116Updated 3 months ago
- Deep learning assisted dynamic mode decomposition☆19Updated 4 years ago
- Koopman Reduced-Order Nonlinear Identification and Control☆92Updated 5 years ago
- ☆20Updated 5 years ago
- Learning Koopman operator by EDMD with trainable dictionary☆27Updated 3 years ago
- Code for ResDMD: data-driven spectral properties of Koopman Operators☆41Updated last year
- A Python package to learn the Koopman operator.☆65Updated last week
- Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…☆43Updated 6 years ago
- Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting☆48Updated 2 years ago
- Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling☆53Updated 4 years ago
- ☆14Updated last year
- ☆21Updated 3 years ago
- Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. Proceedings of the Royal S…☆11Updated last year
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆27Updated 4 years ago
- Port-Hamiltonian Approach to Neural Network Training☆24Updated 6 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆60Updated 3 years ago
- Material for the tutorial on "Physics-Informed Machine Learning (PIML) for Modeling and Control of Dynamical Systems" presented at the Am…☆19Updated last year
- PyTorch Implementation of Lusch et al DeepKoopman☆15Updated 2 years ago
- Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".☆38Updated 3 years ago
- ☆42Updated 7 years ago