  With the emergence of new photonic and plasmonic materials with optimized
properties as well as advanced nanofabrication techniques, nanophotonic devices
are now capable of providing solutions to global challenges in energy
conversion, information technologies, chemical/biological sensing, space
exploration, quantum computing, and secure communication. Addressing grand
challenges poses inherently complex, multi-disciplinary problems with a
manifold of stringent constraints in conjunction with the required system's
performance. Conventional optimization techniques have long been utilized as
powerful tools to address multi-constrained design tasks. One example is
so-called topology optimization that has emerged as a highly successful
architect for the advanced design of non-intuitive photonic structures. Despite
many advantages, this technique requires substantial computational resources
and thus has very limited applicability to highly constrained optimization
problems within high-dimensions parametric space. In our approach, we merge the
topology optimization method with machine learning algorithms such as
adversarial autoencoders and show substantial improvement of the optimization
process by providing unparalleled control of the compact design space
representations. By enabling efficient, global optimization searches within
complex landscapes, the proposed compact hyperparametric representations could
become crucial for multi-constrained problems. The proposed approach could
enable a much broader scope of the optimal designs and data-driven materials
synthesis that goes beyond photonic and optoelectronic applications.
