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- W3135659499 abstract "Modern design of photonic devices is quickly and steadily departing from classical geometries to focus more and more on non-trivial structures and metamaterials. These devices are governed by a multitude of parameters and the optimal design requires to simultaneously consider different figure of merits. In this invited talk we will present our recent work on the application of machine learning tools to the multi-objective optimization of multi-parameter photonic devices. In particular, we will demonstrate the potentiality of dimensionality reduction for the analysis of the complex design space of subwavelength metamaterials devices." @default.
- W3135659499 created "2021-03-15" @default.
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- W3135659499 date "2021-03-05" @default.
- W3135659499 modified "2023-09-26" @default.
- W3135659499 title "Machine-learning dimensionality reduction for multi-objective design of photonic devices" @default.
- W3135659499 doi "https://doi.org/10.1117/12.2586717" @default.
- W3135659499 hasPublicationYear "2021" @default.
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