Data and Code for 'Integrating Physics-Informed and Data-Driven Neural Networks into Earth System Models: A Comparative Study for Compound Flood Simulation at River-Ocean Interfaces'

Installation and Requirements<br><br>PINNs (Physics-Informed Neural Networks)<br>TensorFlow Version: Requires TensorFlow 1.14.0<br>Environment Setup: Use the environment details provided in `requirement_tf1.txt` to set up your Conda environment.<br>conda create --name tf1 --file requirement_tf1.txt<br>conda activate tf1<br><br><br>Data-driven Model<br>TensorFlow Version: Requires TensorFlow 2.17.0<br>Environment Setup: Use the environment details provided in `requirement_tf2.txt` to set up your Conda environment.<br><br>conda create --name tf2 --file requirement_tf2.txt<br>conda activate tf2<br><br><br>### Before training<br>Before running the code, need to create folders to save the model output<br><br>For CNN, create /files/CNN<br><br>For PINNs, create /saved_model<br><br>For saving figures from visualization, create /figures<br><br>Training and Results<br>PINNs<br>Training: To train the model, run:python PINN_test_bnd_uh_Telemac.pypython PINN_test_bnd_uh_Telemac_FDM.py<br>Result Plotting and Comparison: For plotting and comparing results, use:python PINN_plot_comparison.py<br><br><br>Data-driven Model<br>CNN Training: To train the CNN model, execute:python train_CNN.py<br><br>Result Visualization: To visualize the results of the CNN model, run:python predict_CNN.py<br><br>To reproduce all results and figures in the manuscript, please refer to the scripts in analysis/