Zymrael / PortHamiltonianNNLinks
Port-Hamiltonian Approach to Neural Network Training
☆24Updated 5 years ago
Alternatives and similar repositories for PortHamiltonianNN
Users that are interested in PortHamiltonianNN are comparing it to the libraries listed below
Sorting:
- ☆28Updated 2 years ago
- ☆111Updated 4 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆57Updated 3 years ago
- Nonparametric Differential Equation Modeling☆54Updated last year
- Sparse Identification of Nonlinear Dynamics for Hybrid Systems☆25Updated 7 years ago
- Software to train neural networks via Koopman operator theory (see Dogra and Redman "Optimizing Neural Networks via Koopman Operator Theo…☆21Updated 2 years ago
- ☆73Updated 5 years ago
- Python trust-region subproblem solvers for nonlinear optimization☆28Updated last year
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆79Updated 2 years ago
- Learning unknown ODE models with Gaussian processes☆26Updated 7 years ago
- Data-driven dynamical systems toolbox.☆76Updated 2 months ago
- Symplectic Recurrent Neural Networks☆28Updated 2 years ago
- This repository contains code released by DiffEqML Research☆91Updated 3 years ago
- ☆47Updated 4 years ago
- Code for the paper "Rational neural networks", NeurIPS 2020☆27Updated 4 years ago
- ☆28Updated 4 years ago
- Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations wi…☆69Updated last year
- ☆22Updated 11 months ago
- ☆21Updated 5 years ago
- ☆30Updated 3 years ago
- A Python package to learn the Koopman operator.☆61Updated this week
- Differentiable interface to FEniCS for JAX☆58Updated 4 years ago
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆30Updated 3 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆73Updated 4 years ago
- Drop-in replacements for PyTorch nn.Linear for stable learning and inductive priors in physics informed machine learning applications.☆18Updated 2 years ago
- ☆12Updated 3 years ago
- SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations☆21Updated 3 years ago
- ☆21Updated 2 years ago
- Stiff Neural Ordinary Differential Equations☆35Updated 2 years ago
- Refining continuous-in-depth neural networks☆42Updated 3 years ago