| description abstract | Physics-informed neural networks (PINNs) have received increased interest for forward, inverse, and surrogate modeling of problems described by partial differential equations (PDEs). However, their application to multiphysics problem, governed by several coupled PDEs, presents unique challenges that have hindered the robustness and widespread applicability of this approach. Here we investigate the application of PINNs to the forward solution of problems involving thermo–hydro–mechanical (THM) processes in porous media that exhibit disparate spatial and temporal scales in thermal conductivity, hydraulic permeability, and elasticity. In addition, PINNs are faced with the challenges of the multiobjective and nonconvex nature of the optimization problem. To address these fundamental issues, we (1) rewrote the THM governing equations in dimensionless form that is best suited for deep learning algorithms, (2) propose a sequential training strategy that circumvents the need for a simultaneous solution of the multiphysics problem and facilitates the task of optimizers in the solution search, and (3) leveraged adaptive weight strategies to overcome the stiffness in the gradient flow of the multiobjective optimization problem. Finally, we applied this framework to the solution of several synthetic problems in one and two dimensions. | |
