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Review Open access Sep 2026

Physics-Informed Machine Learning in Subsurface Multiphysics Flow Modeling: Integrating Physical Constraints for Accelerated Simulation

Subsurface thermo-hydro-mechanical (THM) coupled processes are fundamental to geomechanics, yet conventional mesh-based methods face high computational costs and limited efficiency in strongly nonlinear simulations and inverse problems. This review examines two representative physics-informed machine learning paradigms...

Lin-Chao Wang, Fei Xiong, F. Dang et al. · 0 citations
Review Open access Aug 2026

Machine Learning-Driven Multi-Scale Modeling and Digital Twin Evolution for Geothermal Reservoirs and Underground Thermal Storage

This review systematically evaluates machine learning as a foundational paradigm for overcoming computational and scale-bridging challenges in geothermal energy and underground thermal storage, highlighting physics-informed machine learning (PIML) and hybrid architectures that embed conservation laws as strict constrai...

Xue Li, Lin Zhu, Wan Zhang et al. · 1 citation

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