Dataset for: Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE

<strong>Based on the Github respostitory: https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main</strong> This repository contains the data used for the paper "Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE"<br> You'll find the csv files for the three simulated (Hydrus 1D) scenarios explained in the paper. These files were processed from the 'Nod_Inf.out' files to csv format. <strong>Acknowledgments</strong><br> The publicly available data used for this study (scenario 1 &amp; 2) as well as the code for the second PINN architecture (based on Dr. Maziar Raissi PINN code) and the code used to transform “Nod_inf.out” files from Hydrus 1D to csv files created by Dr. Toshiyuki Bandai and Dr. Teamrat A. Ghezzehei were helpfulfor this study.