10-15 July 2022 • Denver, Colorado, USA

IEEE AP-S/URSI 2022

10-15 July 2022 • Denver, Colorado, USA

TU-A1.1A.3

Design of Wideband Mushroom Antennas Using Single- and Multi-Objective Bayesian Optimization

Yunjia Zeng, Xianming Qing, Michael Yan Wah Chia, Institute for Infocomm Research, Singapore
Session:
Design and Optimization of Antennas using Machine Learning I
Track:
AP-S: Antennas
Location:
Granite A/B/C (HR)
Presentation Time:
Tue, 12 Jul, 08:40 - 09:00 Denver Time (UTC -7)
Session Co-Chairs:
Zhi Ning Chen, National University of Singapore and Peiqin Liu, National University of Singapore
Presentation
Discussion
Session TU-A1.1A
TU-A1.1A.1: Restoration of Antenna Radiation Pattern Using Conditional Generative Adversarial Network
Yi-Huan Chen, Trung Dung Ha, Danilo Erricolo, Pai-Yen Chen, University of Illinois Chicago, United States
TU-A1.1A.2: Computationally-Efficient and Reliable Antenna Design within the System-by-Design ML-Based Framework
Andrea Massa, Marco Salucci, Pietro Rosatti, Lorenzo Poli, ELEDIA@UniTN - University of Trento, Italy
TU-A1.1A.3: Design of Wideband Mushroom Antennas Using Single- and Multi-Objective Bayesian Optimization
Yunjia Zeng, Xianming Qing, Michael Yan Wah Chia, Institute for Infocomm Research, Singapore
TU-A1.1A.4: Robustness Optimization of Nanophotonic Devices Using Deep Learning
Ronald Jenkins, Sawyer Campbell, Pingjuan Werner, Douglas Werner, The Pennsylvania State University, United States
TU-A1.1A.5: Machine learning based tandem network approach for antenna design
Aggraj Gupta, Chandan Bhat, Uday Khankhoje, IIT Madras, India; Emir Karahan, Kaushik Sengupta, Princeton University, United States
TU-A1.1A.6: A Deep-Learning Characteristic Modes Classification Model for Patch Antennas
Ricardo E. Sendrea, Constantinos L. Zekios, Stavros V. Geogakopoulos, Florida International University, United States
TU-A1.1A.7: Power Pattern to Planar Dipole Array Synthesis Using a Text-to-Image Transformer Based Model
Chen Niu, Puyan Mojabi, University of Manitoba, Canada
TU-A1.1A.8: Supervised Machine Learning Model for Accurate Output Prediction of Various Antenna Designs
Sai Sampreeth Indharapu, Anthony N Caruso, University of Missouri - Kansas City, United States; Kalyan C. Durbhakula, University of Missouri kansas city, United States
TU-A1.1A.9: A Generalized Approach to Real-Time Performance Estimation of Antenna Types Using Deep Learning
Md Rayhan Khan, Constantinos L. Zekios, Shubhendu Bhardwaj, Stavros V. Georgakopoulos, Florida International University, United States
TU-A1.1A.10: Deep Learning based Modeling and Inverse Design for Arbitrary Planar Antenna Structures at RF and Millimeter-Wave
Emir Ali Karahan, Kaushik Sengupta, Princeton University, United States; Aggraj Gupta, Uday K Khankhoje, IIT Madras, India
Resources
No resources available.