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

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. •We propose a Physics-Informed Neural Network (PINN) approach to multiphysics inverse problems.•We adopt a dimensionless form of the governing equations suitable for PINNs.•We propose a sequential training of coupled PDEs for thermo-hydro-mechanical problems.•We apply the method to single-phase and multiphase flow porous media problems.