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...
Main Authors: | Mehmet Akif Tulum, Ahmet Serdar Turk, Peyman Mahouti |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10285868/ |
Similar Items
-
Rapid Design of 3D Reflectarray Antennas by Inverse Surrogate Modeling and Regularization
by: Slawomir Koziel, et al.
Published: (2023-01-01) -
Improved Modeling of Microwave Structures Using Performance-Driven Fully-Connected Regression Surrogate
by: Slawomir Koziel, et al.
Published: (2021-01-01) -
Surrogate-Model-Based Interval Analysis of Spherical Conformal Array Antenna with Power Pattern Tolerance
by: Guangda Ding, et al.
Published: (2022-12-01) -
On Inadequacy of Sequential Design of Experiments for Performance-Driven Surrogate Modeling of Antenna Input Characteristics
by: Anna Pietrenko-Dabrowska, et al.
Published: (2020-01-01) -
Suppressing Side-Lobes of Linear Phased Array of Micro-Strip Antennas with Simulation-Based Optimization
by: Kozieł Sławomir, et al.
Published: (2016-06-01)