Tutorial codes for a physics informed machine learning lecture based on shallow water equation.
All executable code is in the notebook swe_instructor_code_retreat.ipynb. To directly open the notebook in Google Colab, click the badge below.
This repository contains the codes for a physics informed machine learning lecture based on shallow water equation.
We will use different methods to train a U-net to integrate the shallow water equation in time. The methods are:
- Vanilla supervised learning
- Learning the residuals
- Learning fluxes
- Hybrid numerical and machine learning approach
All necessary codes are in the notebook swe_instructor_code_retreat.ipynb. The notebook will guide you through the steps in concepts. Eventually you will hit a cell that on execution displays two buttons "train" and "load results". If you select load results, you will not have to wait for the training to finish, but already pre-trained models will be loaded and you can continue with the next steps. If you wish to play around with hyper parameters and train your own models, you can select the train button, but keep in mind that training (especially on colab) will take some time.
conda env create -f environment.ymlpip install -r requirements.txt