  A physics-informed neural network is presented for poroelastic problems with
coupled flow and deformation processes. The governing equilibrium and mass
balance equations are discussed and specific derivations for two-dimensional
cases are presented. A fully-connected deep neural network is used for
training. Barry and Mercer's source problem with time-dependent fluid
injection/extraction in an idealized poroelastic medium, which has an exact
analytical solution, is used as a numerical example. A random sample from the
analytical solution is used as training data and the performance of the model
is tested by predicting the solution on the entire domain after training. The
deep learning model predicts the horizontal and vertical deformations well
while the error in the predicted pore pressure predictions is slightly higher
because of the sparsity of the pore pressure values.
