Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands
Abstract The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions. Recently, physics-informed neural networks (PINNs),...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | International Journal of Geo-Engineering |
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| Online Access: | https://doi.org/10.1186/s40703-025-00232-w |
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| author | Yuan Feng Jongwan Eun Seunghee Kim Yong-Rak Kim |
| author_facet | Yuan Feng Jongwan Eun Seunghee Kim Yong-Rak Kim |
| author_sort | Yuan Feng |
| collection | DOAJ |
| description | Abstract The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions. Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. In this study, we applied PINNs to tackle hydraulic and thermal transport coupling forward problems in silty sands. A fully connected deep neural network was utilized for training. This neural network model leverages automatic differentiation to apply the governing equations as constraints, based on the mathematical approximations established by the neural network itself. We conducted forward problems and compared the solutions derived from PINNs with those from Finite Element Method (FEM) simulations. The forward problem results demonstrate the PINNs model’s capability in predicting hydraulic transport, heat transport, and thermal–hydraulic coupling in silty sands under various boundary conditions. The PINNs exhibited great performance in simulating the thermal–hydraulic coupling problem. The accuracy of the PINNs solutions shows its potential for simulation in geotechnical engineering. |
| format | Article |
| id | doaj-art-bf36c610ae904aeea1d795b5d1dc839e |
| institution | Kabale University |
| issn | 2198-2783 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Geo-Engineering |
| spelling | doaj-art-bf36c610ae904aeea1d795b5d1dc839e2025-01-19T12:08:18ZengSpringerOpenInternational Journal of Geo-Engineering2198-27832025-01-0116111810.1186/s40703-025-00232-wApplication of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sandsYuan Feng0Jongwan Eun1Seunghee Kim2Yong-Rak Kim3Department of Civil and Environmental Engineering, University of MarylandDepartment of Civil and Environmental Engineering, University of MarylandDepartment of Civil and Environmental Engineering, University of Nebraska-LincolnZachry Department of Civil and Environmental Engineering, Texas A&M UniversityAbstract The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions. Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. In this study, we applied PINNs to tackle hydraulic and thermal transport coupling forward problems in silty sands. A fully connected deep neural network was utilized for training. This neural network model leverages automatic differentiation to apply the governing equations as constraints, based on the mathematical approximations established by the neural network itself. We conducted forward problems and compared the solutions derived from PINNs with those from Finite Element Method (FEM) simulations. The forward problem results demonstrate the PINNs model’s capability in predicting hydraulic transport, heat transport, and thermal–hydraulic coupling in silty sands under various boundary conditions. The PINNs exhibited great performance in simulating the thermal–hydraulic coupling problem. The accuracy of the PINNs solutions shows its potential for simulation in geotechnical engineering.https://doi.org/10.1186/s40703-025-00232-wPhysics-informed neural networksDeep learningHeat transportTH coupling |
| spellingShingle | Yuan Feng Jongwan Eun Seunghee Kim Yong-Rak Kim Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands International Journal of Geo-Engineering Physics-informed neural networks Deep learning Heat transport TH coupling |
| title | Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands |
| title_full | Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands |
| title_fullStr | Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands |
| title_full_unstemmed | Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands |
| title_short | Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands |
| title_sort | application of physics informed neural networks pinns solution to coupled thermal and hydraulic processes in silty sands |
| topic | Physics-informed neural networks Deep learning Heat transport TH coupling |
| url | https://doi.org/10.1186/s40703-025-00232-w |
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