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BEDNARSKÝ, V. RAIDA, Z.
Original Title
Machine Learning for Antenna Design: Combining CST Studio Suite and Python
Type
conference paper
Language
English
Original Abstract
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.
Keywords
CST Studio Suite, Python, PyTochr, TensorFlow, particle swarm optimization (PSO), canonical antenna
Authors
BEDNARSKÝ, V.; RAIDA, Z.
Released
25. 4. 2023
Publisher
BRNO UNIVERSITY OF TECHNOLOGY, FACULTY OF ELECTRICAL ENGINEERING AND COMMUNICATION
Location
Brno
ISBN
978-80-214-6153-6
Book
PROCEEDINGS I OF THE 29TH STUDENT EEICT 2023
Edition
1
Edition number
Pages from
352
Pages to
356
Pages count
5
URL
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_1.pdf
BibTex
@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" }