Color2Struct is a deep learning framework for inverse design of structural colors that improves on tandem networks by 65% in color difference and 48% in short-wave near-infrared reflectivity through sampling bias correction, adaptive loss weighting, and physics-guided inference.
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A review of integrated photonic computing that organizes low- to high-dimensional architectures and argues that exploiting light's full dimensionality offers a path to scalable, energy-efficient information processing.
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Color2Struct: efficient and accurate deep-learning inverse design of structural color with controllable inference
Color2Struct is a deep learning framework for inverse design of structural colors that improves on tandem networks by 65% in color difference and 48% in short-wave near-infrared reflectivity through sampling bias correction, adaptive loss weighting, and physics-guided inference.
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Integrated photonic computing: towards high-dimensional information processing
A review of integrated photonic computing that organizes low- to high-dimensional architectures and argues that exploiting light's full dimensionality offers a path to scalable, energy-efficient information processing.