  Physics-Informed Neural Networks (PINNs) have received increased interest for
forward, inverse, and surrogate modeling of problems described by partial
differential equations (PDE). However, their application to multiphysics
problem, governed by several coupled PDEs, present 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, which
exhibit disparate spatial and temporal scales in thermal conductivity,
hydraulic permeability, and elasticity. In addition, PINNs are faced with the
challenges of the multi-objective and non-convex nature of the optimization
problem. To address these fundamental issues, we: (1)~rewrite 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)~leverage adaptive weight
strategies to overcome the stiffness in the gradient flow of the
multi-objective optimization problem. Finally, we apply this framework to the
solution of several synthetic problems in 1D and~2D.
