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A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion Coefficient and Average Solute Transport Velocity in Porous Media
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  • Yinquan Meng,
  • Jianguo Jiang,
  • Jichun Wu,
  • Dong Wang
Yinquan Meng
School of Earth Sciences and Engineering, Nanjing University
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Jianguo Jiang
School of Earth Sciences and Engineering, Nanjing University

Corresponding Author:jianguo.jiang@nju.edu.cn

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Jichun Wu
Nanjing University
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Dong Wang
Nanjing University
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Abstract

Dispersion coefficients and the average solute transport velocity are pivotal for groundwater solute transport modeling. Accurately and efficiently determining these parameters is challenging due to difficulties in directly correlating them with pore-space structure. To address this issue, we introduced the Physics-enhanced Convolutional Neural Network-Transformer (PhysenCT-Net), an innovative model designed to concurrently estimate the longitudinal dispersion coefficient and average solute transport velocity in three-dimensional porous media. PhysenCT-Net exhibited excellent predictive performance on unseen testing datasets and significantly reduced computational demands. Comprehensive evaluations confirmed its robust generalization across various flow conditions and pore structures. Notably, the longitudinal dispersion coefficient predictions closely align with established empirical relationships involving Péclet number, affirming the model’s physical interpretability and potential to aid in simulating transport phenomena in porous media.