Surrogate modeling for unsaturated infiltration via the physics and equality-constrained artificial neural networks

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外文摘要：Machine learning(ML)provides a new surrogate method for investigating groundwater flow dynamics in unsaturated soils.Traditional pure data-driven methods(e.g.deep neural network,DNN)can provide rapid predictions,but they do require sufficient on-site data for accurate training,and lack interpret-ability to the physical processes within the data.In this paper,we provide a physics and equality-constrained artificial neural network(PECANN),to derive unsaturated infiltration solutions with a small amount of initial and boundary data.PECANN takes the physics-informed neural network(PINN)as a foundation,encodes the unsaturated infiltration physical laws(i.e.Richards equation,RE)into the loss function,and uses the augmented Lagrangian method to constrain the learning process of the solutions of RE by adding stronger penalty for the initial and boundary conditions.Four unsaturated infiltration cases are designed to test the training performance of PECANN,i.e.one-dimensional(1D)steady-state unsaturated infiltration,1D transient-state infiltration,two-dimensional(2D)transient-state infiltra-tion,and 1D coupled unsaturated infiltration and deformation.The predicted results of PECANN are compared with the finite difference solutions or analytical solutions.The results indicate that PECANN can accurately capture the variations of pressure head during the unsaturated infiltration,and present higher precision and robustness than DNN and PINN.It is also revealed that PECANN can achieve the same accuracy as the finite difference method with fewer initial and boundary training data.Addi-tionally,we investigate the effect of the hyperparameters of PECANN on solving RE problem.PECANN provides an effective tool for simulating unsaturated infiltration.

外文关键词：

Richards equation(RE)Unsaturated infiltrationData-driven solutionsNumerical modelingMachine learning(ML)

作者：

Peng Lan、Jingjing Su、Sheng Zhang

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作者单位：

School of Civil Engineering,Central South University,Changsha,410075,China

Discipline of Civil,Surveying and Environmental Engineering,Priority Research Centre for Geotechnical Science and Engineering,The University of Newcastle,Callaghan,NSW,2308,Australia

School of Civil Engineering, Central South University, Changsha 410075, China

Qinghai University,Qinghai,810016,China

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基金：

Science and Technology Innovation Program of Hunan ProvinceHunan Provincial Postgraduate Research and Innovation Project国家自然科学基金青年基金High-Performance Computing Center of Central South University

项目编号：

2023RC1017CX2022010952208378

出版年：

2024

DOI：

10.1016/j.jrmge.2023.09.014

岩石力学与岩土工程学报(英文版)

中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD

影响因子：0.404

ISSN：1674-7755

年,卷(期)：2024.16(6)