Physics-aware-AI / Symplectic-ODENetLinks
☆47Updated 4 years ago
Alternatives and similar repositories for Symplectic-ODENet
Users that are interested in Symplectic-ODENet are comparing it to the libraries listed below
Sorting:
- A framework for neural network control of dynamical systems over graphs.☆56Updated 3 years ago
- ☆111Updated 4 years ago
- Symplectic Recurrent Neural Networks☆28Updated 2 years ago
- [ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control☆91Updated 4 years ago
- Offline Contextual Bayesian Optimization☆14Updated 2 years ago
- Companion code to "Learning Stable Deep Dynamics Models" (Manek and Kolter, 2019)☆33Updated 5 years ago
- Nonparametric Differential Equation Modeling☆55Updated last year
- This repository contains code released by DiffEqML Research☆91Updated 3 years ago
- Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling☆52Updated 3 years ago
- A PyTorch library for all things nonlinear control and reinforcement learning.☆47Updated 4 years ago
- Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting☆47Updated last year
- Official implementation for our paper "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"☆19Updated 3 years ago
- ☆15Updated 4 years ago
- This gym provides implementations of various PDEs for easy testing and comparison of data-driven and classical PDE control algorithms.☆33Updated 3 months ago
- ODE2VAE: Deep generative second order ODEs with Bayesian neural networks☆130Updated last year
- This repository contains the source code to perform Geometry-aware Bayesian Optimization (GaBO) on Riemannian manifolds.☆53Updated 3 years ago
- Learning unknown ODE models with Gaussian processes☆26Updated 7 years ago
- ☆28Updated 3 years ago
- Experiment code for "Continuous-Time Model-Based Reinforcement Learning"☆54Updated last year
- Refining continuous-in-depth neural networks☆42Updated 3 years ago
- Consistent Koopman Autoencoders☆74Updated 2 years ago
- Port-Hamiltonian Approach to Neural Network Training☆24Updated 5 years ago
- Koopman Kernels for Learning Dynamical Systems from Trajectory Data☆31Updated last year
- Re-implementation of Hamiltonian Generative Networks paper☆33Updated 3 years ago
- Data-driven dynamical systems toolbox.☆77Updated this week
- Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…☆42Updated 6 years ago
- ☆21Updated 6 years ago
- A Python package to learn the Koopman operator.☆63Updated this week
- Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.☆81Updated 6 months ago
- Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight☆55Updated 3 years ago