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.
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-Guided Neural Networks
- Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
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.
git clone [email protected]:cemac/LIFD_Torch_PINNS.git
cd LIFD_Torch_PINNS
conda env create -f PINN_pytorch.yml
conda activate PINN
jupyter-notebookThis 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.
LIFD_ENV_ML_NOTEBOOKS by cemac is licensed under a Creative Commons Attribution 4.0 International License.
