  Here, we present a new approach based on manifold learning for knowledge
discovery and inverse design with minimal complexity in photonic
nanostructures. Our approach builds on studying sub-manifolds of responses of a
class of nanostructures with different design complexities in the latent space
to obtain valuable insight about the physics of device operation to guide a
more intelligent design. In contrast to the current methods for inverse design
of photonic nanostructures, which are limited to pre-selected and usually
over-complex structures, we show that our method allows evolution from an
initial design towards the simplest structure while solving the inverse
problem.
