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1 June 2022 Forward modeling for metamaterial design using feature-based machine learning
Francesco Ferranti
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Proceedings Volume 12130, Metamaterials XIII; 1213009 (2022) https://doi.org/10.1117/12.2621890
Event: SPIE Photonics Europe, 2022, Strasbourg, France
Abstract
Machine learning techniques have been proposed in the literature for the modeling of photonic devices. In this paper, a modeling technique based on system identification, feature extraction, and machine learning methods is proposed for the design of photonic devices. Design features of interest are extracted based on a system identification step that uses a few samples of the electromagnetic device response. This system identification step allows saving computational resources significantly while collecting the data needed for the further machine learning step. Modeling design features instead of the wavelength-dependent device response as a function of the design parameters allows compacting the output space of interest in neural networks and reducing related model complexity issues. These features can be modeled as a function of design parameters by means of neural networks. The generated neural networks are of very limited complexity. Design features represent very valuable and meaningful information for designers. Numerical results successfully validate the proposed technique.
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Francesco Ferranti "Forward modeling for metamaterial design using feature-based machine learning", Proc. SPIE 12130, Metamaterials XIII, 1213009 (1 June 2022); https://doi.org/10.1117/12.2621890
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KEYWORDS
Data modeling
Feature extraction
System identification
Neural networks
Machine learning
Metamaterials
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