Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands

Feng, Yuan; Eun, Jongwan; Kim, Seunghee; Kim, Yong-Rak

doi:10.1186/s40703-025-00232-w

Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands

Journal Article · Wed Jan 15 04:00:00 EST 2025 · International Journal of Geo-Engineering (Online)

DOI:https://doi.org/10.1186/s40703-025-00232-w· OSTI ID:2500990

Feng, Yuan; Eun, Jongwan; Kim, Seunghee; Kim, Yong-Rak

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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: NE0008954

OSTI ID:: 2500990

Journal Information:: International Journal of Geo-Engineering (Online), Journal Name: International Journal of Geo-Engineering (Online) Journal Issue: 1 Vol. 16; ISSN 2198-2783

Publisher:: Springer Science + Business MediaCopyright Statement

Country of Publication:: Germany

Language:: English

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