Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 7.71 MB
Add to list
  1. Search results
  2. KARMA : kernel-aided rational macromo...

KARMA : kernel-aided rational macromodeling of noisy electrical frequency response data

Thijs Ullrick (UGent) , Dirk Deschrijver (UGent) , Wim Bogaerts (UGent) and Tom Dhaene (UGent)
(2025) IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES.
Author
Organization
Project
Abstract
Gaussian processes (GPs) have attracted significant interest for macromodeling frequency-domain responses of linear time-invariant (LTI) systems. Yet, a unified and practical approach to extend their use to the time domain has remained elusive. This article introduces kernel-aided rational macromodeling (KARMA), a novel framework that constructs compact, multiport rational representations suitable for circuit simulation directly from frequency response data. By leveraging the unique properties of complex-valued rational kernels, KARMA fuses the advantages of kernel-based nonparametric methods with the physical properties of traditional rational parametric macromodeling techniques. The framework comprises critical components such as joint hyperparameter optimization, passivity enforcement, and model order reduction (MOR). Moreover, the probabilistic nature of GPs enables KARMA to generate uncertainty-aware macromodels, making the framework particularly effective in noisy or data-scarce settings.
Keywords
Frequency-domain system identification, Gaus-sian processes (GPs), linear time-invariant (LTI) systems, macromodeling, macromodeling, physics-informed kernels, physics-informed kernels, rational modeling, rational modeling, rational modeling, PASSIVITY ENFORCEMENT, MODEL-REDUCTION, APPROXIMATION, SYSTEMS, IDENTIFICATION, ALGORITHM, CIRCUITS

Downloads

  • Download
    8926 acc.pdf
    • full text (Accepted manuscript)
    • |
    • open access
    • |
    • PDF
    • |
    • 7.71 MB

Citation

Please use this url to cite or link to this publication:

MLA
Ullrick, Thijs, et al. “KARMA : Kernel-Aided Rational Macromodeling of Noisy Electrical Frequency Response Data.” IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2025, doi:10.1109/TMTT.2025.3621537.
APA
Ullrick, T., Deschrijver, D., Bogaerts, W., & Dhaene, T. (2025). KARMA : kernel-aided rational macromodeling of noisy electrical frequency response data. IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES. https://doi.org/10.1109/TMTT.2025.3621537
Chicago author-date
Ullrick, Thijs, Dirk Deschrijver, Wim Bogaerts, and Tom Dhaene. 2025. “KARMA : Kernel-Aided Rational Macromodeling of Noisy Electrical Frequency Response Data.” IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES. https://doi.org/10.1109/TMTT.2025.3621537.
Chicago author-date (all authors)
Ullrick, Thijs, Dirk Deschrijver, Wim Bogaerts, and Tom Dhaene. 2025. “KARMA : Kernel-Aided Rational Macromodeling of Noisy Electrical Frequency Response Data.” IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES. doi:10.1109/TMTT.2025.3621537.
Vancouver
1.
Ullrick T, Deschrijver D, Bogaerts W, Dhaene T. KARMA : kernel-aided rational macromodeling of noisy electrical frequency response data. IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES. 2025;
IEEE
[1]
T. Ullrick, D. Deschrijver, W. Bogaerts, and T. Dhaene, “KARMA : kernel-aided rational macromodeling of noisy electrical frequency response data,” IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2025.
@article{01K9YTQD3QMR3C3GKX8EKF8WQF,
  abstract     = {{Gaussian processes (GPs) have attracted significant interest for macromodeling frequency-domain responses of linear time-invariant (LTI) systems. Yet, a unified and practical approach to extend their use to the time domain has remained elusive. This article introduces kernel-aided rational macromodeling (KARMA), a novel framework that constructs compact, multiport rational representations suitable for circuit simulation directly from frequency response data. By leveraging the unique properties of complex-valued rational kernels, KARMA fuses the advantages of kernel-based nonparametric methods with the physical properties of traditional rational parametric macromodeling techniques. The framework comprises critical components such as joint hyperparameter optimization, passivity enforcement, and model order reduction (MOR). Moreover, the probabilistic nature of GPs enables KARMA to generate uncertainty-aware macromodels, making the framework particularly effective in noisy or data-scarce settings.}},
  author       = {{Ullrick, Thijs and Deschrijver, Dirk and Bogaerts, Wim and Dhaene, Tom}},
  issn         = {{0018-9480}},
  journal      = {{IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES}},
  keywords     = {{Frequency-domain system identification,Gaus-sian processes (GPs),linear time-invariant (LTI) systems,macromodeling,macromodeling,physics-informed kernels,physics-informed kernels,rational modeling,rational modeling,rational modeling,PASSIVITY ENFORCEMENT,MODEL-REDUCTION,APPROXIMATION,SYSTEMS,IDENTIFICATION,ALGORITHM,CIRCUITS}},
  language     = {{eng}},
  pages        = {{18}},
  title        = {{KARMA : kernel-aided rational macromodeling of noisy electrical frequency response data}},
  url          = {{http://doi.org/10.1109/TMTT.2025.3621537}},
  year         = {{2025}},
}
Altmetric
View in Altmetric
Web of Science
Times cited: