  The physics informed neural network (PINN) is evolving as a viable method to
solve partial differential equations. In the recent past PINNs have been
successfully tested and validated to find solutions to both linear and
non-linear partial differential equations (PDEs). However, the literature lacks
detailed investigation of PINNs in terms of their representation capability. In
this work, we first test the original PINN method in terms of its capability to
represent a complicated function. Further, to address the shortcomings of the
PINN architecture, we propose a novel distributed PINN, named DPINN. We first
perform a direct comparison of the proposed DPINN approach against PINN to
solve a non-linear PDE (Burgers' equation). We show that DPINN not only yields
a more accurate solution to the Burgers' equation, but it is found to be more
data-efficient as well. At last, we employ our novel DPINN to two-dimensional
steady-state Navier-Stokes equation, which is a system of non-linear PDEs. To
the best of the authors' knowledge, this is the first such attempt to directly
solve the Navier-Stokes equation using a physics informed neural network.
