  We demonstrate that embedding physics-driven constraints into machine
learning process can dramatically improve accuracy and generalizability of the
resulting model. Physics-informed learning is illustrated on the example of
analysis of optical modes propagating through a spatially periodic composite.
The approach presented can be readily utilized in other situations mapped onto
an eigenvalue problem, a known bottleneck of computational electrodynamics.
Physics-informed learning can be used to improve machine-learning-driven
design, optimization, and characterization, in particular in situations where
exact solutions are scarce or are slow to come up with.
