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/