  This study introduces a novel physics-informed neural networks (PINNs)
framework designed to model coupled-field problems specifically tailored for
heterogeneous poroelastic media. Firstly, a composite neural network is
developed where distinct neural networks are dedicated to predicting
displacement and pressure variables for each material, employing identical
activation functions but trained separately across all other parameters.
Secondly, we handle the challenges of heterogeneous material interfaces by the
Interface- PINNs (I-PINNs) framework, where different activation functions
across any material interface are prescribed to ensure that the discontinuities
in solution fields and gradients are accurately captured. We compare the
modified PINNs framework with the conventional approach on two one-dimensional
benchmark examples for poroelasticity in heterogeneous media. Furthermore, we
assess a single neural network architecture, comparing it against the composite
neural network proposed in this work. These examples show that the proposed
framework demonstrates superior approximation accuracy in both displacements
and pressures, and better convergence behavior.
