Spectral bayesian optimization using a physics-informed rational Szego kernel for microwave design
- Author
- Yens Lindemans (UGent) , Thijs Ullrick (UGent) , Ivo Couckuyt (UGent) , Tim Pattyn (UGent) , Dirk Deschrijver (UGent) , Dries Vande Ginste (UGent) and Tom Dhaene (UGent)
- Organization
- Project
- Abstract
- Microwave device design increasingly relies on surrogate modeling to accelerate optimization and reduce costly electromagnetic (EM) simulations. This article presents a spectral Bayesian optimization (SBO) framework leveraging a physics-informed Gaussian process (GP) with a rational complex-valued Szeg & ouml; kernel and input warping to enhance surrogate accuracy and data efficiency. Unlike conventional methods that model scalar objectives, our approach directly learns the complex-valued frequency response, enforcing causality and Hermitian symmetry. Effectiveness is demonstrated in two cases: a zig-zag microstrip bandpass filter optimized for magnitude response and a passive differential equalizer optimized for both transmission magnitude and group delay. By embedding prior physics and modeling directly in the frequency domain, the method enables accurate, sample-efficient optimization of frequency-dependent behavior. This work shows how physics-informed Bayesian optimization (BO) can significantly improve microwave device design efficiency.
- Keywords
- Kernel, Optimization, Scattering parameters, Gaussian processes, Frequency response, Bayes methods, Covariance matrices, Vectors, Training, Microwave filters, Bayesian optimization (BO), Gaussian process (GP), machine learning (ML), microwave devices, physics-informed, Szeg & ouml, kernel
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01K6FAGNKQNN498G62G1XPHARC
- MLA
- Lindemans, Yens, et al. “Spectral Bayesian Optimization Using a Physics-Informed Rational Szego Kernel for Microwave Design.” IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY, vol. 15, no. 9, 2025, pp. 1836–46, doi:10.1109/TCPMT.2025.3592441.
- APA
- Lindemans, Y., Ullrick, T., Couckuyt, I., Pattyn, T., Deschrijver, D., Vande Ginste, D., & Dhaene, T. (2025). Spectral bayesian optimization using a physics-informed rational Szego kernel for microwave design. IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY, 15(9), 1836–1846. https://doi.org/10.1109/TCPMT.2025.3592441
- Chicago author-date
- Lindemans, Yens, Thijs Ullrick, Ivo Couckuyt, Tim Pattyn, Dirk Deschrijver, Dries Vande Ginste, and Tom Dhaene. 2025. “Spectral Bayesian Optimization Using a Physics-Informed Rational Szego Kernel for Microwave Design.” IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY 15 (9): 1836–46. https://doi.org/10.1109/TCPMT.2025.3592441.
- Chicago author-date (all authors)
- Lindemans, Yens, Thijs Ullrick, Ivo Couckuyt, Tim Pattyn, Dirk Deschrijver, Dries Vande Ginste, and Tom Dhaene. 2025. “Spectral Bayesian Optimization Using a Physics-Informed Rational Szego Kernel for Microwave Design.” IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY 15 (9): 1836–1846. doi:10.1109/TCPMT.2025.3592441.
- Vancouver
- 1.Lindemans Y, Ullrick T, Couckuyt I, Pattyn T, Deschrijver D, Vande Ginste D, et al. Spectral bayesian optimization using a physics-informed rational Szego kernel for microwave design. IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY. 2025;15(9):1836–46.
- IEEE
- [1]Y. Lindemans et al., “Spectral bayesian optimization using a physics-informed rational Szego kernel for microwave design,” IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY, vol. 15, no. 9, pp. 1836–1846, 2025.
@article{01K6FAGNKQNN498G62G1XPHARC,
abstract = {{Microwave device design increasingly relies on surrogate modeling to accelerate optimization and reduce costly electromagnetic (EM) simulations. This article presents a spectral Bayesian optimization (SBO) framework leveraging a physics-informed Gaussian process (GP) with a rational complex-valued Szeg & ouml; kernel and input warping to enhance surrogate accuracy and data efficiency. Unlike conventional methods that model scalar objectives, our approach directly learns the complex-valued frequency response, enforcing causality and Hermitian symmetry. Effectiveness is demonstrated in two cases: a zig-zag microstrip bandpass filter optimized for magnitude response and a passive differential equalizer optimized for both transmission magnitude and group delay. By embedding prior physics and modeling directly in the frequency domain, the method enables accurate, sample-efficient optimization of frequency-dependent behavior. This work shows how physics-informed Bayesian optimization (BO) can significantly improve microwave device design efficiency.}},
author = {{Lindemans, Yens and Ullrick, Thijs and Couckuyt, Ivo and Pattyn, Tim and Deschrijver, Dirk and Vande Ginste, Dries and Dhaene, Tom}},
issn = {{2156-3950}},
journal = {{IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY}},
keywords = {{Kernel,Optimization,Scattering parameters,Gaussian processes,Frequency response,Bayes methods,Covariance matrices,Vectors,Training,Microwave filters,Bayesian optimization (BO),Gaussian process (GP),machine learning (ML),microwave devices,physics-informed,Szeg & ouml,kernel}},
language = {{eng}},
number = {{9}},
pages = {{1836--1846}},
title = {{Spectral bayesian optimization using a physics-informed rational Szego kernel for microwave design}},
url = {{http://doi.org/10.1109/TCPMT.2025.3592441}},
volume = {{15}},
year = {{2025}},
}
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