| Summary: | Physics-informed Neural Network (PINN) model and its derivatives, altogether, build a new category of machine learning (ML) problems: scientific machine learning (SciML). SciML had been active on solving different problems in engineering and applied science including thermodynamics, fluid mechanics and optics and photonics. The thesis focuses on the development of SciML methods that enable the inverse design of photonic devices and eventually, photonic metasurfaces (MS), a very prosperous and prospective subject in optics and photonics. This thesis focuses on SciML methods that successfully predict the transmission pattern of light through photonic devices; in particular, fully connected (FC) simple neural networks (NN) successfully predicts the transmission pattern of light as planar, timeinvariant wave, through parallel stacked multi-layered thin-film filters by incorporation of continuity of electric field and magnetic field accompanying the transmission of light. This thesis also shows the potency of other formulations of derivatives of PINN, particularly, the weak adversarial network (WAN). The network structure, along with the incorporation of continuity, sets a new paradigm in photonic inverse design: different from traditional adjoint optimization (AO) using finite element method (FEM), in particular, finite difference time domain (FDTD) methods to produce transmission pattern of the light, the NN based paradigm is better supported by GPU acceleration tool that facilitates the floating point operations, thus the prediction (or reconstruction, used interchangeably hereafter) of transmission patterns. The thesis also covers a ML based method, distance metric learning (DML), that can help the inverse design by modeling different types of responses of different types of photonic devices. DML can aid the inverse design process by picking the designs that yields peak intensity at desirable wavelength of different types of responses that have different properties, like FWHM. Altogether, the thesis paves a way ...
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