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Published July 21, 2023 | Version v1
Dataset Open

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

Description

Based on the Github respostitory: https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main

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"
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.

Acknowledgments
The publicly available data used for this study (scenario 1 & 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.

Files

Hydrus 1D processed data.zip

Files (80.2 MB)

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md5:ca07dfce26b3181f62bb627f4f630335
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