Physics-informed-GPR data augmentation framework for flower-shaped antenna performance optimization and prediction using machine learning models.
- DOI
- 10.1038/s41598-026-60513-x
- Published
- 2026 Jul 12
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-60513-x,
title = {Physics-informed-GPR data augmentation framework for flower-shaped antenna performance optimization and prediction using machine learning models.},
author = {Ayalew LG and Kumlachew TY and Chaka DK and Demesa EG},
year = {2026},
journal = {Scientific reports},
doi = {10.1038/s41598-026-60513-x},
url = {https://doi.org/10.1038/s41598-026-60513-x}
}RIS
TY - JOUR TI - Physics-informed-GPR data augmentation framework for flower-shaped antenna performance optimization and prediction using machine learning models. AU - Ayalew LG AU - Kumlachew TY AU - Chaka DK AU - Demesa EG PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-60513-x UR - https://doi.org/10.1038/s41598-026-60513-x ER -
APA
LG, A., TY, K., DK, C., & EG, D. (2026). Physics-informed-GPR data augmentation framework for flower-shaped antenna performance optimization and prediction using machine learning models.. Scientific reports. https://doi.org/10.1038/s41598-026-60513-x
Source records
- pubmed · retrieved 2026-09-26T02:11:05.387Z