  This short note describes the concept of guided training of deep neural
networks (DNNs) to learn physically reasonable solutions. DNNs are being widely
used to predict phenomena in physics and mechanics. One of the issues of DNNs
is that their output does not always satisfy physical equations. One approach
to consider physical equations is adding a residual of equations into the loss
function; this is called physics-informed neural network (PINN). One feature of
PINNs is that the physical equations and corresponding residual must be
implemented as part of a neural network model. In addition, the residual does
not always converge to a small value. The proposed model is a physics-guided
generative adversarial network (PG-GAN) that uses a GAN architecture in which
physical equations are used to judge whether the neural network's output is
consistent with physics. The proposed method was applied to a simple problem to
assess its potential usability.
