Data and Code for 'A Comparative Study of Physics-Informed and Data-Driven Neural Networks for Compound Flood Simulation at River-Ocean Interfaces: A Case Study of Hurricane Irene'

Installation and Requirements

PINNs (Physics-Informed Neural Networks)
TensorFlow Version: Requires TensorFlow 1.14.0
Environment Setup: Use the environment details provided in `requirement_tf1.txt` to set up your Conda environment.

conda create --name tf1 --file requirement_tf1.txt
conda activate tf1

Data-driven Model
TensorFlow Version: Requires TensorFlow 2.17.0
Environment Setup: Use the environment details provided in `requirement_tf2.txt` to set up your Conda environment.

conda create --name tf2 --file requirement_tf2.txt
conda activate tf2

### Before training
Before running the code, need to create folders to save the model output

For CNN, create /files/CNN

For PINNs, create /saved_model

For saving figures from visualization, create /figures

Training and Results

PINNs
Training: To train the model, run:
python PINN_test_bnd_uh_Telemac.py
python PINN_test_bnd_uh_Telemac_FDM.py

Result Plotting and Comparison: For plotting and comparing results, use:
python PINN_plot_comparison.py

Data-driven Model
CNN Training: To train the CNN model, execute:
python train_CNN.py

Result Visualization: To visualize the results of the CNN model, run:
python predict_CNN.py

To reproduce all results and figures in the manuscript, please refer to the scripts in analysis/