  We describe a Physics-Informed Neural Network (PINN) that simulates the flow
induced by the astronomical tide in a synthetic port channel, with dimensions
based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models
aim to combine the knowledge of physical systems and data-driven machine
learning models. This is done by training a neural network to minimize the
residuals of the governing equations in sample points. In this work, our flow
is governed by the Navier-Stokes equations with some approximations. There are
two main novelties in this paper. First, we design our model to assume that the
flow is periodic in time, which is not feasible in conventional simulation
methods. Second, we evaluate the benefit of resampling the function evaluation
points during training, which has a near zero computational cost and has been
verified to improve the final model, especially for small batch sizes. Finally,
we discuss some limitations of the approximations used in the Navier-Stokes
equations regarding the modeling of turbulence and how it interacts with PINNs.
