  In this paper, the optimal choice of the interior penalty parameter of the
discontinuous Galerkin finite element methods for both the elliptic problems
and the Biot's systems are studied by utilizing the neural network and machine
learning. It is crucial to choose the optimal interior penalty parameter, which
is not too small or not too large for the stability, robustness, and efficiency
of the numerical discretized solutions. Both linear regression and nonlinear
artificial neural network methods are employed and compared using several
numerical experiments to illustrate the capability of our proposed
computational framework. This framework is an integral part of a developing
automated numerical simulation platform because it can automatically identify
the optimal interior penalty parameter. Real-time feedback could also be
implemented to update and improve model accuracy on the fly.
