Data Driven Surrogate Modeling of Phase Array Antennas Using Deep Learning for Millimetric Band Applications

Phased Array Antenna (PAA) technology plays an important role in fields such as radar, 5G and satellite or any application which requires wide bandwidth and high gain. However, achieving such design is a difficult and complex task that requires an accurate calculation and combination of results obta...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE access Ročník 11; s. 114415 - 114423
Hlavní autoři:	Tulum, Mehmet Akif, Turk, Ahmet Serdar, Mahouti, Peyman
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Antenna arrays Antennas Artificial intelligence Broadband Broadband antennas Computational efficiency Computational electromagnetics Computational modeling Computing costs Computing time data driven modelling Data models Deep learning High gain Machine learning Modelling Optimization phased array antenna Phased arrays Prediction models Radar antennas Simulation surrogate modelling
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!