Multiphysics‐Informed Neural Networks for Coupled Soil Hydrothermal Modeling
Abstract Soil water and heat transport are two physical processes that are described by the Richardson–Richards equation and heat transport equation, respectively. Soil water and heat motion directly control transport or indirectly influence parameters. The physics‐informed neural network (PINN) is...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2022WR031960 |
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| author | Yanling Wang Liangsheng Shi Xiaolong Hu Wenxiang Song Lijun Wang |
| author_facet | Yanling Wang Liangsheng Shi Xiaolong Hu Wenxiang Song Lijun Wang |
| author_sort | Yanling Wang |
| collection | DOAJ |
| description | Abstract Soil water and heat transport are two physical processes that are described by the Richardson–Richards equation and heat transport equation, respectively. Soil water and heat motion directly control transport or indirectly influence parameters. The physics‐informed neural network (PINN) is a new method that combines deep learning and physical laws that approximates and learns physical dynamics better than traditional data‐driven deep learning methods. In this study, we propose multiphysics‐informed neural networks for soil water‐heat systems, in which the soil moisture and temperature information complement each other well. With our framework, existing soil moisture neural networks are improved to reduce their dependency on the soil moisture measurement density. Furthermore, soil moisture data are employed to promote soil temperature dynamic learning and soil thermal conductivity estimation. Moreover, soil temperature data assist in recovering the nonlinearity of the soil hydraulic conductivity through hydrothermal coupling constraints, allowing better estimations of the soil water flux density. The gradient‐based annealing method is applied to adapt the loss function, which satisfactorily balances the water‐heat transport governing equation constraints on the neural networks. The robustness and generalizability of our framework are examined under diverse scenarios. This work demonstrates the mutual compensation of multisource data in coupled physical processes in a deep learning framework and highlights the significance of appropriate multiphysical constraints designed for nonlinear parameter recovery in PINNs. |
| format | Article |
| id | doaj-art-472d1d1884b54d6fa958e57b0cc3c3f3 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-472d1d1884b54d6fa958e57b0cc3c3f32025-08-20T03:30:02ZengWileyWater Resources Research0043-13971944-79732023-01-01591n/an/a10.1029/2022WR031960Multiphysics‐Informed Neural Networks for Coupled Soil Hydrothermal ModelingYanling Wang0Liangsheng Shi1Xiaolong Hu2Wenxiang Song3Lijun Wang4State Key Laboratory of Water Resources and Hydropower Engineering Sciences Wuhan University Wuhan ChinaState Key Laboratory of Water Resources and Hydropower Engineering Sciences Wuhan University Wuhan ChinaState Key Laboratory of Water Resources and Hydropower Engineering Sciences Wuhan University Wuhan ChinaState Key Laboratory of Water Resources and Hydropower Engineering Sciences Wuhan University Wuhan ChinaState Key Laboratory of Water Resources and Hydropower Engineering Sciences Wuhan University Wuhan ChinaAbstract Soil water and heat transport are two physical processes that are described by the Richardson–Richards equation and heat transport equation, respectively. Soil water and heat motion directly control transport or indirectly influence parameters. The physics‐informed neural network (PINN) is a new method that combines deep learning and physical laws that approximates and learns physical dynamics better than traditional data‐driven deep learning methods. In this study, we propose multiphysics‐informed neural networks for soil water‐heat systems, in which the soil moisture and temperature information complement each other well. With our framework, existing soil moisture neural networks are improved to reduce their dependency on the soil moisture measurement density. Furthermore, soil moisture data are employed to promote soil temperature dynamic learning and soil thermal conductivity estimation. Moreover, soil temperature data assist in recovering the nonlinearity of the soil hydraulic conductivity through hydrothermal coupling constraints, allowing better estimations of the soil water flux density. The gradient‐based annealing method is applied to adapt the loss function, which satisfactorily balances the water‐heat transport governing equation constraints on the neural networks. The robustness and generalizability of our framework are examined under diverse scenarios. This work demonstrates the mutual compensation of multisource data in coupled physical processes in a deep learning framework and highlights the significance of appropriate multiphysical constraints designed for nonlinear parameter recovery in PINNs.https://doi.org/10.1029/2022WR031960soil moisturesoil heat transportmultiphysics‐informed neural networkshydrothermal coupling |
| spellingShingle | Yanling Wang Liangsheng Shi Xiaolong Hu Wenxiang Song Lijun Wang Multiphysics‐Informed Neural Networks for Coupled Soil Hydrothermal Modeling Water Resources Research soil moisture soil heat transport multiphysics‐informed neural networks hydrothermal coupling |
| title | Multiphysics‐Informed Neural Networks for Coupled Soil Hydrothermal Modeling |
| title_full | Multiphysics‐Informed Neural Networks for Coupled Soil Hydrothermal Modeling |
| title_fullStr | Multiphysics‐Informed Neural Networks for Coupled Soil Hydrothermal Modeling |
| title_full_unstemmed | Multiphysics‐Informed Neural Networks for Coupled Soil Hydrothermal Modeling |
| title_short | Multiphysics‐Informed Neural Networks for Coupled Soil Hydrothermal Modeling |
| title_sort | multiphysics informed neural networks for coupled soil hydrothermal modeling |
| topic | soil moisture soil heat transport multiphysics‐informed neural networks hydrothermal coupling |
| url | https://doi.org/10.1029/2022WR031960 |
| work_keys_str_mv | AT yanlingwang multiphysicsinformedneuralnetworksforcoupledsoilhydrothermalmodeling AT liangshengshi multiphysicsinformedneuralnetworksforcoupledsoilhydrothermalmodeling AT xiaolonghu multiphysicsinformedneuralnetworksforcoupledsoilhydrothermalmodeling AT wenxiangsong multiphysicsinformedneuralnetworksforcoupledsoilhydrothermalmodeling AT lijunwang multiphysicsinformedneuralnetworksforcoupledsoilhydrothermalmodeling |