  Physics-informed neural networks (PINNs) employed in fluid mechanics deal
primarily with stationary boundaries. This hinders the capability to address a
wide range of flow problems involving moving bodies. To this end, we propose a
novel extension, which enables PINNs to solve incompressible flows with
time-dependent moving boundaries. More specifically, we impose Dirichlet
constraints of velocity at the moving interfaces and define new loss functions
for the corresponding training points. Moreover, we refine training points for
flows around the moving boundaries for accuracy. This effectively enforces the
no-slip condition of the moving boundaries. With an initial condition, the
extended PINNs solve unsteady flow problems with time-dependent moving
boundaries and still have the flexibility to leverage partial data to
reconstruct the entire flow field. Therefore, the extended version inherits the
amalgamation of both physics and data from the original PINNs. With a series of
typical flow problems, we demonstrate the effectiveness and accuracy of the
extended PINNs. The proposed concept allows for solving inverse problems as
well, which calls for further investigations.
