  Deep learning techniques with neural networks have been used effectively in
computational fluid dynamics (CFD) to obtain solutions to nonlinear
differential equations. This paper presents a physics-informed neural network
(PINN) approach to solve the Blasius function. This method eliminates the
process of changing the non-linear differential equation to an initial value
problem. Also, it tackles the convergence issue arising in the conventional
series solution. It is seen that this method produces results that are at par
with the numerical and conventional methods. The solution is extended to the
negative axis to show that PINNs capture the singularity of the function at
$\eta=-5.69$
