EPFL CS-433 Machine Learning: Project 2
ICLR 2019 Reproducibility Challenge: Learning Neural PDE Solvers with Convergence Guarantees
This is the repository for the second project in the CS-433 class at EPFL.
We tried to reproduce the results of a paper handed for the ICLR conference: https://openreview.net/forum?id=rklaWn0qK7
The link to our issue is: https://github.com/reproducibility-challenge/iclr_2019/issues/90
conda env create -f environment.yml --name <your chosen name>
source activate <your chosen name>
# Note: not everything is listed here, use this as a guidance. ├── environment.yml # environment file ├── nnpde │ ├── main.ipynb # main notebook, entry point │ └── nnpde │ ├── __init__.py │ ├── geometries.py # geometries: shapes and boundaries │ ├── helpers.py # more project based helpers │ ├── iterative_methods.py # definition of iterative solver │ ├── metrics.py │ ├── model.py # model definition │ ├── model_testing.py │ ├── problems.py # definition problems │ └── utils # various helpers │ ├── __init__.py │ ├── jupyter.ipynb │ ├── jupyter.py │ ├── logs.py │ ├── misc.py │ └── plots.py ├── README.md # this file ├── report # latex script, plots, etc. └── references └── paper.pdf # paper on which this is based
The notebook files were converted using this script, but should be viewed as a notebook.
General comments about the code
The deep learning part is implemented in PyTorch, therefore when in doubt it's a PyTorch tensor.
Authors (in alphabetical order)
Francesco Bardi, Samuel Edler von Baussnern, Emiljano Gjiriti