mfinzi / constrained-hamiltonian-neural-networksLinks
☆107Updated 4 years ago
Alternatives and similar repositories for constrained-hamiltonian-neural-networks
Users that are interested in constrained-hamiltonian-neural-networks are comparing it to the libraries listed below
Sorting:
- Refining continuous-in-depth neural networks☆39Updated 3 years ago
- This repository contains code released by DiffEqML Research☆90Updated 3 years ago
- Symplectic Recurrent Neural Networks☆28Updated 2 years ago
- ☆45Updated 4 years ago
- Nonparametric Differential Equation Modeling☆53Updated last year
- Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations wi…☆69Updated last year
- Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)☆87Updated 2 years ago
- ☆29Updated 2 years ago
- Convex potential flows☆83Updated 3 years ago
- Re-implementation of Hamiltonian Generative Networks paper☆33Updated 2 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- Repo to the paper "Lie Point Symmetry Data Augmentation for Neural PDE Solvers"☆50Updated 2 years ago
- Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting☆46Updated last year
- ☆34Updated 3 years ago
- Riemannian Convex Potential Maps☆67Updated 2 years ago
- Turning SymPy expressions into PyTorch modules.☆145Updated last year
- ☆27Updated 4 years ago
- Port-Hamiltonian Approach to Neural Network Training☆24Updated 5 years ago
- Official implementation for our paper "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"☆19Updated 2 years ago
- Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"☆58Updated 8 months ago
- code for "Neural Conservation Laws A Divergence-Free Perspective".☆38Updated 2 years ago
- ☆20Updated 2 years ago
- Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight☆52Updated 3 years ago
- Equation Learner, a neural network approach to symbolic regression☆78Updated 8 months ago
- Code for "Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory," NeurIPS, 2021.☆16Updated 3 years ago
- Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.☆77Updated last month
- ☆30Updated 2 years ago
- Differentiable interface to FEniCS for JAX☆54Updated 4 years ago
- By introducing a differentiable contact model, DiffCoSim extends the applicability of Lagrangian/Hamiltonian-inspired neural networks to …☆36Updated 2 years ago
- Learning unknown ODE models with Gaussian processes☆26Updated 6 years ago