DecodEPFL / HamiltonianNet
PyTorch implementation of Hamiltonian deep neural networks.
☆19Updated 3 years ago
Alternatives and similar repositories for HamiltonianNet
Users that are interested in HamiltonianNet are comparing it to the libraries listed below
Sorting:
- By introducing a differentiable contact model, DiffCoSim extends the applicability of Lagrangian/Hamiltonian-inspired neural networks to …☆36Updated 2 years ago
- ☆19Updated 2 years ago
- ☆15Updated 4 years ago
- This repository contains the source code to perform Geometry-aware Bayesian Optimization (GaBO) on Riemannian manifolds.☆52Updated 3 years ago
- ☆34Updated 3 years ago
- Symplectic Recurrent Neural Networks☆28Updated 2 years ago
- Nonparametric Differential Equation Modeling☆53Updated last year
- ☆29Updated 2 years ago
- Supplementary code for the paper "Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces"☆42Updated last year
- A repository with implementations of major papers on Gaussian Process regression models, implemented from scratch in Python, notably incl…☆14Updated 2 years ago
- ☆40Updated last year
- Code repo for "Kernel Interpolation for Scalable Online Gaussian Processes"☆62Updated 4 years ago
- Re-implementation of Hamiltonian Generative Networks paper☆33Updated 2 years ago
- [ICML 2022] Learning Efficient and Robust Ordinary Differential \\ Equations via Invertible Neural Networks☆10Updated 2 years ago
- TorchFSM: Fourier Spectral Method with PyTorch☆43Updated this week
- Training neural networks to disentangle conservative and dissipative dynamics☆10Updated 3 years ago
- Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight☆52Updated 3 years ago
- Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".☆29Updated 3 months ago
- ☆30Updated 7 months ago
- ☆107Updated 4 years ago
- Koopman Kernels for Learning Dynamical Systems from Trajectory Data☆26Updated last year
- A PyTorch library for all things nonlinear control and reinforcement learning.☆46Updated 3 years ago
- A collection of graph neural networks implementations in JAX☆32Updated last year
- Implementation of the work Variational multiple shooting for Bayesian ODEs with Gaussian processes☆12Updated 2 years ago
- Port-Hamiltonian Approach to Neural Network Training☆24Updated 5 years ago
- Code for "Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory," NeurIPS, 2021.☆16Updated 3 years ago
- ☆45Updated 4 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- Code for Deep Structured Mixtures of Gaussian Processes (DSMGPs)☆11Updated 3 years ago
- Exploring how to to deal with uncertain inputs with gaussian process regression models.☆27Updated 4 years ago