These set of notebooks explore Physics Informed Neural Networks to explore Partial Differential Equations
This tutorial has been split into 3 tutorials.
If you are unfamiliar with some of the concept covered in this tutorial it's recommended to read through the background reading below either as you go through the notebook or beforehand. These links are also contained with in the notebooks
- Introduction to Neural Networks
- Physics Guided Neural Networks
- Physics-Informed Neural Networks: A Deep LearningFramework for Solving Forward and Inverse ProblemsInvolving Nonlinear Partial Differential Equations
If you want a quick look at the contents inside the notebook before deciding to run it please view the pdfs generated (note some html code not fully rendered)
- PNNs 1D Heat Equation Example nonML
- PNNs 1D Heat Equation Example
- PNNs Navier Stokes example
- PNNs Navier Stokes Hidden Fluid Mechanics
This notebook is designed to run on a laptop with no special hardware required therefore recommended to do a local installation as outline in the repository howtorun and jupyter_notebooks sections.
These notebooks require some additional data from the PINNs repository
If you have not already then in your gitbash or terminal please run the following code in the LIFD_ENV_ML_NOTEBOOKS directory via the terminal(mac or linux) or git bash (windows)
git submodule init
git submodule update --init --recursiveIf this does not work please clone the PINNs repository into your Physics_Informed_Neural_Networks folder on your computer
If you're already familiar with git, anaconda and virtual environments the environment you need to create is found in CNN.yml and the code below to install activate and launch the notebook
conda env create -f PINN.yml
conda activate PINN
jupyter-notebook