  Physics-informed neural network (PINN) is a data-driven approach to solve
equations.
  It is successful in many applications; however, the accuracy of the PINN is
not satisfactory when it is used to solve multiscale equations.
  Homogenization is a way of approximating a multiscale equation by a
homogenized equation without multiscale property; it includes solving cell
problems and the homogenized equation.
  The cell problems are periodic; and we propose an oversampling strategy which
greatly improves the PINN accuracy on periodic problems.
  The homogenized equation has constant or slow dependency coefficient and can
also be solved by PINN accurately.
  We hence proposed a 3-step method to improve the PINN accuracy for solving
multiscale problems with the help of the homogenization.
  We apply our method to solve three equations which represent three different
homogenization.
  The results show that the proposed method greatly improves the PINN accuracy.
  Besides, we also find that the PINN aided homogenization may achieve better
accuracy than the numerical methods driven homogenization; PINN hence is a
potential alternative to implementing the homogenization.
