AntennAI: Towards Prior-knowledge Guided Machine-learning Enabled Generative Antenna Design
The rapid evolution of wireless technologies continues to drive the demand for increasingly advanced antenna solutions. Simultaneously, breakthroughs in artificial intelligence (AI), particularly in generative neural network methods, are opening new frontiers in antenna design.
This talk begins with a brief overview of the challenges involved in high-performance antenna design, emphasizing the need to explore broader design spaces to increase degrees of freedom and uncover novel solutions. To address the resulting complexity, transformative deep learning (DL) models, a subset of machine learning (ML) within AI, are introduced as tools for generative synthesis in antenna design.
Three design exemplars utilizing generative adversarial networks (GANs) are presented, demonstrating innovative approaches to metacell design within metasurfaces. These are achieved through pixelization strategies and DL-driven algorithms. A particular focus is placed on integrating prior knowledge (PK) into the DL-based synthesis process, illustrating its effectiveness in generating metacells for metalens applications.
The presentation further highlights how metalenses synthesized using PK-guided DL methods exhibit breakthrough performance and offer enhanced functionalities in metalens antenna design. The talk concludes with a forward-looking perspective on the integration of AI and antenna engineering, outlining its transformative potential and the emerging challenges and opportunities in this evolving interdisciplinary domain.
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- Technologiepark Zwijnaarde
- Gent, Oost-Vlaanderen
- Belgium 9052
- Building: 126
- Room Number: IDLab9
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Speakers
Professor Zhi Ning Chen
AntennAI: Towards Prior-knowledge Guided Machine-learning Enabled Generative Antenna Design
The rapid evolution of wireless technologies continues to drive the demand for increasingly advanced antenna solutions. Simultaneously, breakthroughs in artificial intelligence (AI), particularly in generative neural network methods, are opening new frontiers in antenna design.
This talk begins with a brief overview of the challenges involved in high-performance antenna design, emphasizing the need to explore broader design spaces to increase degrees of freedom and uncover novel solutions. To address the resulting complexity, transformative deep learning (DL) models, a subset of machine learning (ML) within AI, are introduced as tools for generative synthesis in antenna design.
Three design exemplars utilizing generative adversarial networks (GANs) are presented, demonstrating innovative approaches to metacell design within metasurfaces. These are achieved through pixelization strategies and DL-driven algorithms. A particular focus is placed on integrating prior knowledge (PK) into the DL-based synthesis process, illustrating its effectiveness in generating metacells for metalens applications.
The presentation further highlights how metalenses synthesized using PK-guided DL methods exhibit breakthrough performance and offer enhanced functionalities in metalens antenna design. The talk concludes with a forward-looking perspective on the integration of AI and antenna engineering, outlining its transformative potential and the emerging challenges and opportunities in this evolving interdisciplinary domain.
Biography:
Zhi Ning Chen received his two Ph.D. degrees in 1993 and 2003 from institutions in China and Japan, respectively. He currently serves as a Provost’s Chair and Professor in the Department of Electrical and Computer Engineering and as Director of the Advanced Research and Technology Innovation Centre at the National University of Singapore.
Professor Chen has authored or co-authored 776 journal and conference papers, along with seven books. His current research interests include electromagnetic metamaterials and metasurfaces, metantennas, AI algorithms for generative antenna design, antennAI, and wireless systems.
Professor Chen was elevated to IEEE Fellow in 2007, elected Fellow of the Academy of Engineering, Singapore in 2019, and is a Fellow and Vice President of the Asia-Pacific Artificial Intelligence Association (2021). Among numerous academic and technical honors, he received the IEEE AP-S John Kraus Antenna Award in 2021 and the EurAAP Antenna Award in 2025.
In addition to his research achievements, Professor Chen has played a leading role in international conferences. He served as the General Chair of the 2021 IEEE AP-S Symposium (Singapore) and is the Founding General Chair of several key conferences, including the IEEE International Workshop on Antenna Technology (2005), the Asia-Pacific Conference on Antennas and Propagation (2012), and the Marina Forum on Metantennas+X (2021).
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Address:Zwijnaarde 126, 9052 Gent, Belgium, , Gent, Oost-Vlaanderen, Belgium, 9052