Abstract
This paper examines how training data affects machine learning-assisted antenna pattern prediction under mutual coupling. For demonstration, a neural network-based approach is used to predict the embedded pattern of a central patch antenna element near randomly distributed patches. It is shown that when the full-wave simulated dataset size is excessively reduced, the high prediction error in the validation set may become a critical issue. Maintaining sufficient accuracy in pattern prediction with a relatively small dataset remains an open challenge.
| Original language | English |
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| Title of host publication | Proceedings of the 4th URSI Atlantic RadioScience Conference – AT-RASC 2024 |
| Number of pages | 4 |
| ISBN (Electronic) | 978-9-4639-6-8102 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 4th URSI Atlantic RadioScience Conference - Gran Canaria, Spain Duration: 19 May 2024 → 24 May 2024 Conference number: 4 |
Publication series
| Name | 2024 4th URSI Atlantic Radio Science Meeting, AT-RASC 2024 |
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Conference
| Conference | 4th URSI Atlantic RadioScience Conference |
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| Abbreviated title | AT-RASC 2024 |
| Country/Territory | Spain |
| City | Gran Canaria |
| Period | 19/05/24 → 24/05/24 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.