Reproducible material for An effective physics-informed neural operator framework for predicting wavefields - Xiao Ma, Tariq Alkhalifah
This repository is organized as follows:
- 📂 asset: folder containing logo;
- 📂 data: Instructions on how to retrieve the data
- 📂 notebooks: set of jupyter notebooks reproducing the experiments in the paper (see below for more details);
- 📂 neuralseismic_xiao: set of python scripts used to run multiple experiments ...
The following notebooks or file 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 - 🐍 train_cno.py: training script for the CNO-based model (handles data loading, model initialization, loss definition, and the full training/validation loop).
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 were conducted on a SLURM-managed GPU cluster equipped with Intel® Xeon® CPUs @ 2.10 GHz and a single NVIDIA A100 GPU per job allocation. Different environment configurations may be required for other workstation, cluster, or GPU architectures.