  The physics informed neural networks (PINNs) has been widely utilized to
numerically approximate PDE problems. While PINNs has achieved good results in
producing solutions for many partial differential equations, studies have shown
that it does not perform well on phase field models. In this paper, we
partially address this issue by introducing a modified physics informed neural
networks. In particular, they are used to numerically approximate
Allen-Cahn-Ohta-Kawasaki (ACOK) equation with a volume constraint.
