Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks

• URL: http://arxiv.org/abs/2209.03276v1
• Date: Wed, 7 Sep 2022 16:28:12 GMT
• Title: Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks
• Authors: Danial Amini, Ehsan Haghighat, Ruben Juanes
• Abstract summary: We propose a solution strategy for parameter identification in thermo-hydro-mechanical processes using physics-informed neural networks (PINNs) We employ a dimensionless form of the THM governing equations that is particularly well suited for the inverse problem, and we leverage the sequential multiphysics PINN solver we developed in previous work. We report the excellent performance of the proposed sequential PINN-THM inverse solver, thus paving the way for the application of PINNs to inverse modeling of complex nonlinear multiphysics problems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a solution strategy for parameter identification in multiphase thermo-hydro-mechanical (THM) processes in porous media using physics-informed neural networks (PINNs). We employ a dimensionless form of the THM governing equations that is particularly well suited for the inverse problem, and we leverage the sequential multiphysics PINN solver we developed in previous work. We validate the proposed inverse-modeling approach on multiple benchmark problems, including Terzaghi's isothermal consolidation problem, Barry-Mercer's isothermal injection-production problem, and nonisothermal consolidation of an unsaturated soil layer. We report the excellent performance of the proposed sequential PINN-THM inverse solver, thus paving the way for the application of PINNs to inverse modeling of complex nonlinear multiphysics problems.

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