  Sub-wavelength diffractive optics meta-optics present a multi-scale optical
system, where the behavior of constituent sub-wavelength scatterers, or
meta-atoms, need to be modelled by full-wave electromagnetic simulations,
whereas the whole meta-optical system can be modelled using ray/ wave optics.
Current simulation techniques for large-scale meta-optics rely on the local
phase approximation (LPA), where the coupling between dissimilar meta-atoms are
completely neglected. Here we introduce a physics-informed neural network,
which can efficiently model the meta-optics while still incorporating all of
the coupling between meta-atoms. Unlike existing deep learning techniques which
generally predict the mean transmission and reflection coefficients of
meta-atoms, we predict the full electro-magnetic field distribution. We
demonstrate the efficacy of our technique by designing 1mm aperture cylindrical
meta-lenses exhibiting higher efficiency than the ones designed under LPA. We
experimentally validated the maximum intensity improvement (up to $53\%$) of
the inverse-designed meta-lens. Our reported method can design large aperture
$(\sim 10^4-10^5\lambda)$ meta-optics in a reasonable time (approximately 15
minutes on a graphics processing unit) without relying on any approximation.
