Forward modeling for metamaterial design using feature-based machine learning

Onderzoeksoutput: Conference paperResearch


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.
Originele taal-2English
TitelForward modeling for metamaterial design using feature-based machine learning
RedacteurenK.F. MacDonald, I. Staude, A.V. Zayats
Aantal pagina's8
ISBN van elektronische versie9781510651364
ISBN van geprinte versie978-1-5106-5136-4
StatusPublished - 2022
EvenementConference on Metamaterials XIII - Electr. Network
Duur: 4 apr 202220 mei 2022


ConferenceConference on Metamaterials XIII


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