Detail výsledku
Machine Learning for Antenna Design: Combining CST Studio Suite and Python
Raida Zbyněk, prof. Dr. Ing., UREL (FEKT)
The design and optimization of antennas is a
complex and time-consuming process which combines an
electromagnetic analysis to evaluate cost functions and a machine
learning to consequently improve designs. In this paper, CST
Studio Suite performs the numerical analysis, and Python scripts
implement other steps. Python executes numerical operations,
automatically generates models, and supports the CST analyses
without requiring user’s interaction. Ultimately, the approach is
aimed to utilize Python’s libraries PyTochr and TensorFlow to
automate antenna designs, which can be leveraged by artificial
intelligence, at a later stage.
CST Studio Suite, Python, PyTochr, TensorFlow,
particle swarm optimization (PSO), canonical antenna
@inproceedings{BUT188126,
author="Vojtěch {Bednarský} and Zbyněk {Raida}",
title="Machine Learning for Antenna Design: Combining CST Studio Suite and Python",
booktitle="PROCEEDINGS I OF THE 29TH STUDENT EEICT 2023",
year="2023",
series="1",
number="1",
pages="352--356",
publisher="BRNO UNIVERSITY OF TECHNOLOGY, FACULTY OF ELECTRICAL ENGINEERING AND COMMUNICATION",
address="Brno",
isbn="978-80-214-6153-6",
url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_1.pdf"
}