Multi-objective artificial-intelligence-based parameter tuning of antennas using variable-fidelity machine learning
Abstract Multi-objective optimization (MO) is an important topic in contemporary antenna design. Due to the reliance on computationally-expensive electromagnetic (EM) simulations, the use of conventional algorithms is prohibitive. These costs can be reduced by appropriate algorithmic tools involving...
Saved in:
| Main Authors: | Slawomir Koziel, Anna Pietrenko-Dabrowska, Stanislaw Szczepanski |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-05657-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Globalized parameter tuning of microwave passives by dimensionality-reduced surrogates and multi-fidelity simulations
by: Slawomir Koziel, et al.
Published: (2025-07-01) -
Rapid global antenna design by simplex regressors and multi-resolution simulations
by: Slawomir Koziel, et al.
Published: (2025-04-01) -
Global miniaturization of broadband antennas by prescreening and machine learning
by: Slawomir Koziel, et al.
Published: (2024-11-01) -
Mobile Robot Positioning with Wireless Fidelity Fingerprinting and Explainable Artificial Intelligence
by: Hüseyin Abacı, et al.
Published: (2024-12-01) -
Cost-efficient behavioral modeling of antennas by means of global sensitivity analysis and dimensionality reduction
by: Slawomir Koziel, et al.
Published: (2025-01-01)