Skip to content

README copy.md

Latest commit

 

History

History
41 lines (27 loc) · 1.71 KB

README copy.md

File metadata and controls

41 lines (27 loc) · 1.71 KB

Reproducible material for An effective physics-informed neural operator1 framework for predicting wavefields - Author Xiao Ma, Author Tariq Alkhalifah

Click here to access the Project Report. Authentication to the Restricted Area filespace is required.

Project structure

This repository is organized as follows:

  • 📂 package: python library containing routines for ....;
  • 📂 asset: folder containing logo;
  • 📂 data: folder containing data (or instructions on how to retrieve the data
  • 📂 notebooks: set of jupyter notebooks reproducing the experiments in the paper (see below for more details);
  • 📂 scripts: set of python scripts used to run multiple experiments ...

Notebooks

The following notebooks are provided:

  • 📙 openfwi_xHz_cno_no_pde.ipynb: notebook performing results with no pde loss;
  • 📙 openfwi_xHz_cno.ipynb: notebook performing results with pde loss

Getting started 👾 🤖

To ensure reproducibility of the results, we suggest using the environment.yml file when creating an environment.

Simply run:

./install_env.sh

It will take some time, if at the end you see the word Done! on your terminal you are ready to go.

Remember to always activate the environment by typing:

conda activate my_env

Disclaimer: All experiments have been carried on a Intel(R) Xeon(R) CPU @ 2.10GHz equipped with a single NVIDIA A100 GPU. Different environment configurations may be required for different combinations of workstation and GPU.