Publication detail

Machine Learning for Antenna Design: Combining CST Studio Suite and Python

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

1

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"
}