Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations

TL;DR

This paper introduces a physics-informed neural network approach for solving forward and backward advection-dispersion equations, demonstrating high accuracy and improved performance over traditional methods, especially at high Péclet numbers.

Contribution

The paper presents a discretization-free PINN method for coupled advection-dispersion and Darcy flow equations, incorporating space-dependent hydraulic conductivity and backward ADE solutions.

Findings

PINN achieves less than 1% error for forward ADEs.

Outperforms conventional methods at Péclet numbers > 100.

Incorporating measurements improves backward ADE accuracy by over 50%.

Abstract

We propose a discretization-free approach based on the physics-informed neural network (PINN) method for solving coupled advection-dispersion and Darcy flow equations with space-dependent hydraulic conductivity. In this approach, the hydraulic conductivity, hydraulic head, and concentration fields are approximated with deep neural networks (DNNs). We assume that the conductivity field is given by its values on a grid, and we use these values to train the conductivity DNN. The head and concentration DNNs are trained by minimizing the residuals of the flow equation and ADE and using the initial and boundary conditions as additional constraints. The PINN method is applied to one- and two-dimensional forward advection-dispersion equations (ADEs), where its performance for various P\'{e}clet numbers ($Pe$) is compared with the analytical and numerical solutions. We find that the PINN method is accurate with errors of less than 1% and outperforms some conventional discretization-based methods for $Pe$ larger than 100. Next, we demonstrate that the PINN method remains accurate for the backward ADEs, with the relative errors in most cases staying under 5% compared to the reference concentration field. Finally, we show that when available, the concentration measurements can be easily incorporated in the PINN method and significantly improve (by more than 50% in the considered cases) the accuracy of the PINN solution of the backward ADE.