  In this paper, we present and compare four methods to enforce Dirichlet
boundary conditions in Physics-Informed Neural Networks (PINNs) and Variational
Physics-Informed Neural Networks (VPINNs). Such conditions are usually imposed
by adding penalization terms in the loss function and properly choosing the
corresponding scaling coefficients; however, in practice, this requires an
expensive tuning phase. We show through several numerical tests that modifying
the output of the neural network to exactly match the prescribed values leads
to more efficient and accurate solvers. The best results are achieved by
exactly enforcing the Dirichlet boundary conditions by means of an approximate
distance function. We also show that variationally imposing the Dirichlet
boundary conditions via Nitsche's method leads to suboptimal solvers.
