https://github.com/cemac/lifd_torch_pinns
Physics_Informed_Neural_Networks
Science Score: 23.0%
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Low similarity (13.1%) to scientific vocabulary
Repository
Physics_Informed_Neural_Networks
Basic Info
- Host: GitHub
- Owner: cemac
- License: cc-by-4.0
- Language: Jupyter Notebook
- Default Branch: main
- Size: 23.2 MB
Statistics
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Leeds Institute for Fluid Dynamics Machine Learning For Earth Sciences
Physics-Informed Neural Networks
This set of computational notebooks explores how Physics-Informed Neural Networks can be applied to Partial Differential Equations (PDEs).
This resource consists of three tutorials split across three separate Jupyter notebooks.
Recommended Background Reading
If you are unfamiliar with some of the concepts covered in this tutorial, it's recommended to consult the background reading listed below, either as you go through the notebooks or beforehand. The following links are also contained within the notebooks:
- Introduction to Neural Networks
- Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
Quick look
Quick start
If you're already familiar with Git, Anaconda and virtual environments, the environment you need to create is found in PINN_pytorch.yml. The code below will install, activate and launch the notebook. The .yml file has been tested on the latest linux, macOS and Windows operating systems.
This notebook is based on two papers: Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations and Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations with the help of Fergus Shone and Michael Macraild.
These tutorials will go through solving Partial Differential Equations using Physics-Informed Neural Networks, focusing on the Burgers Equation and a more complex example using the Navier Stokes Equation.
bash
git clone https://github.com/cemac/LIFD_Torch_PINNS.git
cd LIFD_Torch_PINNS
conda env create -f PINN_pytorch.yml
conda activate PINN
jupyter-notebook
Installation and Requirements
This notebook is designed to run on a laptop with no special hardware required. It is therefore recommended to perform a local installation as outlined in the repository howtorun and jupyter_notebooks sections.
Licence information
LIFDENVML_NOTEBOOKS by CEMAC is licensed under a Creative Commons Attribution 4.0 International License.
Acknowledgements
Owner
- Name: Centre for Environmental Modelling And Computation
- Login: cemac
- Kind: organization
- Location: Leeds
- Website: www.cemac.leeds.ac.uk
- Twitter: CEMAC_Leeds
- Repositories: 53
- Profile: https://github.com/cemac
software to support environmental science
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