Physics Informed Machine Learning Team

Pinned Repositories

DeePyMoD
Language:Python44 4 3011
DeePyMoD_tensorflow
This implementation of DeePyMoD is no longer maintained! We switched to a PyTorch based implementation: https://github.com/PhIMaL/DeePyMoD_torch
Language:Python24 3 07
DeePyMoD_torch
Pytorch implementation of the DeepMoD algorithm: [arXiv:1904.09406]
Language:Jupyter Notebook31 7 216
Docker-images
Language:Shell0 1 00
docker-py
A Python library for the Docker Engine API
Language:Python0 1 00
network_derivs
Provides feed-forward NN layers which also calculate the derivatives w.r.t to the input.
Language:Jupyter Notebook2 1 00
phimal.github.io
Agency Theme for Jekyll
Language:JavaScript0 1 00
phimal_utilities
Language:Jupyter Notebook0 1 00
Project-Template
Language:Shell0 1 00
temporal_normalizing_flows
Language:Python110

Physics Informed Machine Learning Team's Repositories

PhIMaL/DeePyMoD
Language:Python44 4 3011
PhIMaL/DeePyMoD_torch
Pytorch implementation of the DeepMoD algorithm: [arXiv:1904.09406]
Language:Jupyter Notebook31 7 216
PhIMaL/DeePyMoD_tensorflow
This implementation of DeePyMoD is no longer maintained! We switched to a PyTorch based implementation: https://github.com/PhIMaL/DeePyMoD_torch
Language:Python24 3 07
PhIMaL/temporal_normalizing_flows
Language:Python110
PhIMaL/network_derivs
Provides feed-forward NN layers which also calculate the derivatives w.r.t to the input.
Language:Jupyter Notebook2 1 00
PhIMaL/Docker-images
Language:Shell0 1 00
PhIMaL/docker-py
A Python library for the Docker Engine API
Language:Python0 1 00
PhIMaL/phimal.github.io
Agency Theme for Jekyll
Language:JavaScript0 1 00
PhIMaL/phimal_utilities
Language:Jupyter Notebook0 1 00
PhIMaL/Project-Template
Language:Shell0 1 00