  Physics informed neural networks (PINNs) have drawn attention in recent years
in engineering problems due to their effectiveness and ability to tackle the
problems without generating complex meshes. PINNs use automatic differentiation
to evaluate differential operators in conservation laws and hence do not need
to have a discretization scheme. Using this ability, PINNs satisfy governing
laws of physics in the loss function without any training data. In this work,
we solve various incompressible thermal convection problems including real
applications and problems with comparable numerical or analytical results. We
first consider a channel problem with an analytical solution to show the
accuracy of our network. Then, we solve a thermal convection problem in a
closed enclosure in which the flow is only due to the temperature gradients on
the boundaries. Lastly, we consider steady and unsteady partially blocked
channel problems that resemble industrial applications to power electronics.

Fluid Dynamics