Adaptive Frequency Sampling Based on Local Rational Modeling for Microwave Electromagnetic Simulations

Peumans, Dries; De Keersmaeker, Sander; Busschots, Cedric; Rolain, Yves; Ferranti, Francesco

doi:10.1109/tmtt.2024.3355270

IEEE Trans. Microwave Theory Techn.

2024

DOI: 10.1109/tmtt.2024.3355270

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Adaptive Frequency Sampling Based on Local Rational Modeling for Microwave Electromagnetic Simulations

Dries Peumans,

Sander De Keersmaeker,

Cedric Busschots

et al.

Abstract: with details of the nature of the infringement. We will investigate the claim and if justified, we will take the appropriate steps.

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Wavelength-dependent responses and machine learning in nanophotonics modeling

Ferranti

2024

Machine Learning in Photonics

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Machine learning techniques have been proposed in the literature for the modeling of photonic devices. These techniques can be used to speed up the design process. The data samples needed to build machine learning models are collected from electromagnetic simulations. Electromagnetic solvers can result computationally expensive and therefore minimizing the computational effort needed to collect these data samples is an important aspect. Using frequency-domain electromagnetic solvers to collect data samples requires a suitable sampling of the wavelength variable to avoid undersampling and oversampling phenomena. An adaptive frequency-domain sampling approach for nanophotonic applications is illustrated in this work.

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Wavelength-dependent responses and machine learning in nanophotonics modeling

Ferranti

2024

Machine Learning in Photonics

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Adaptive Frequency Sampling Based on Local Rational Modeling for Microwave Electromagnetic Simulations

Abstract: with details of the nature of the infringement. We will investigate the claim and if justified, we will take the appropriate steps.

Cited by 1 publication

References 32 publications

Wavelength-dependent responses and machine learning in nanophotonics modeling

Wavelength-dependent responses and machine learning in nanophotonics modeling

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