Fig. 13: Different encoding methods for AI-driven metasurface design. | npj Nanophotonics

Fig. 13: Different encoding methods for AI-driven metasurface design.

From: From performance to structure: a comprehensive survey of advanced metasurface design for next-generation imaging

Fig. 13: Different encoding methods for AI-driven metasurface design.The alternative text for this image may have been generated using AI.

a Parametric encoding based on geometrical parameters for metasurface design355. b Vector encoding to represent high-dimensional metasurface parameters in optimization tasks358. c Image encoding that represents metasurface design as pixel matrices for optimization with deep learning360. d Graph encoding used to model the interactions between metasurface elements in a network structure363. e Mesh encoding to discretize metasurfaces into grid-based elements for high-precision simulations364. f Sequence encoding to model metasurface designs through ordered data for use in sequence-based neural networks365.

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