  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.
