Sample-efficient Deep Learning for Surrogate-assisted Evolutionary Optimization of Nanostructures

Published: 30 April 2020| Version 1 | DOI: 10.17632/8h832nrkfy.1
Contributor:
Ravi hegde

Description

A Deep Neural Network surrogate-assisted Differential Evolution algorithm for a broadband anti-reflection coating thin-film multilayer optic design. Novel training loss functions, that emphasize a model's ability to predict a structurally similar response, are used. The folder makes available the code, jupyter notebooks, datasets, plotting routines for data display.

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Categories

Differential Evolution, Nanophotonics, Deep Learning

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