SINTEF / pseudo-hamiltonian-neural-networksLinks
The package phlearn for modelling pseudo-Hamiltonian systems by pseudo-Hamiltonian neural networks (PHNN), for ODEs and PDEs
☆17Updated 5 months ago
Alternatives and similar repositories for pseudo-hamiltonian-neural-networks
Users that are interested in pseudo-hamiltonian-neural-networks are comparing it to the libraries listed below
Sorting:
- ☆11Updated 5 months ago
- ☆11Updated last month
- Code for "Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains"☆22Updated last year
- SINDy-SA framework: enhancing nonlinear system identification with sensitivity analysis☆11Updated 3 months ago
- This is the implementation of the RecFNO.☆21Updated 2 years ago
- Separabale Physics-Informed DeepONets in JAX☆10Updated 8 months ago
- ☆22Updated 3 weeks ago
- Code for paper Sparse identification of nonlinear dynamics with Shallow Recurrent Decoder Networks.☆30Updated last week
- Learning with Higher Expressive Power than Neural Networks (On Learning PDEs)☆16Updated 4 years ago
- ☆17Updated last year
- ☆42Updated 2 years ago
- ☆13Updated 8 months ago
- Learning two-phase microstructure evolution using neural operators and autoencoder architectures☆23Updated last year
- This is the repository for the code used in the ICML23 paper called "Achieving High Accuracy with PINNs via Energy Natural Gradient Desce…☆21Updated 9 months ago
- Code and files related to random side projects☆21Updated 3 years ago
- Neural Galerkin☆16Updated last year
- ☆14Updated 3 years ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆35Updated 3 years ago
- Simple demo on implementing data driven and physics informed Deep O Nets in pytorch☆15Updated last year
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆26Updated 3 years ago
- Multiwavelets-based operator model☆63Updated 3 years ago
- ☆30Updated last month
- Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid …☆36Updated 2 years ago
- Practicum on Supervised Learning in Function Spaces☆33Updated 3 years ago
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆66Updated 3 months ago
- Generative Learning for Forecasting the Dynamics of High Dimensional Complex Systems☆35Updated 5 months ago
- [ICLR 2025] Neural Operator-Assisted Computational Fluid Dynamics in PyTorch☆47Updated last month
- DON-LSTM: Multi-Resolution Learning with DeepONets and Long-Short Term Memory Neural Networks☆10Updated 9 months ago
- PyTorch implemention of the Position-induced Transformer for operator learning in partial differential equations☆20Updated 2 months ago
- Deep learning assisted dynamic mode decomposition☆20Updated 3 years ago