.. _ex3_submission: ===================== Submission Exercise 3 ===================== **Deadline**: Please see the time table in `Ilias `_. As submission for this exercise upload your files (named below) in a zip-folder on Ilias. Solution hand-in ---------------- Please hand in a zip-directory named `NameOne_NameTwo_NameThree_Abgabe3` containing: - your trained model as `model.pt` - the `min_max.yaml` file containing minimum and maximum values of all input and output channels in previously described format - a python file (`mymodel.py`) containing a function (`init_my_model()`) to initialize your model - a results-file containing - the measured evaluation metrics of your best model for train, val, test RMSE, MAE on the un-normed predicted u, v fields - number of trainable parameters of your best model - train vs. validation loss graphs - 1-2 sample data point predictions and comparisons to their labels (optional: visualization of vector fields via `plt.quiver` on the un-normed velocity fields) - visualizations of the three out-of-distribution test samples (have an explanation of what you can see at hand for the oral exam) Rules ----- The same rules as for exercise 1 apply regarding commented code, correct results, etc. No third-party libraries are allowed except for numpy, torch, matplotlib, pathlib, yaml, PIL.Image, typing, pyvista. Questions for the interview ("Abnahme") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * What happens if you apply your model to an out-of-distribution Reynolds number and boundary condition? * What happens if you apply your model to a larger spatial domain? * What happens if you apply your model to an input field where the passing-by-velocity is not at the top but at the bottom (or side wall)? * What can you learn from having a high MSE loss but a low MAE loss and vice versa?