PINNs are artificial neural networks capable of solving problems in physics that are described by partial differential equations (PDEs) like the Navier-Stokes equations in fluid mechanics. PINNs include physical constraints in the loss functions in order to obtain solutions that obey the laws of physics and fulfill boundary conditions. After training and validation, PINNs can perform orders of magnitude faster than conventional CFD methods when solving fluid flow and heat transfer problems.