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GitHub - DeanHazineh DFlat: DFlat is a forward and inverse design . . . DFlat is an auto-differentiable design framework for flat optics, specially geared to the design of dielectric metasurfaces for imaging and sensing tasks This package was first introduced in a 2022 manuscript available at arxiv
D-Flat: A Differentiable Flat-Optics Framework for End-to-End . . . Optical metasurfaces are planar substrates with custom-designed, nanoscale features that selectively modulate incident light with respect to direction, wavelength, and polarization When coupled with photodetectors and appropriate post-capture processing, they provide a means to create computational imagers and sensors that are exceptionally small and have distinctive capabilities We
dflat-opt · PyPI DFlat is an auto-differentiable design framework for flat optics, specially geared to the design of dielectric metasurfaces for imaging and sensing tasks This package was first introduced in a 2022 manuscript available at arxiv
「Dflat」Structured Data Store for Mobile Unlike Core Data, Dflat is built from ground-up with Swift You can express your data model by taking full advantage of the Swift language Thus, a native support for struct (product-type), enum (sum-type), with type-checked queries and observing with Combine
GitHub - xumingledad DFlat: D-Flat is a forward and inverse design . . . Examples for inverse design are provided in DFlat examples Additional examples will be provided in the future (we welcome community made examples) For developers and researchers, a script to train neural models can be found in DFlat dflat neural_optical_layer core trainer_models py