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DC Field | Value | Language |
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dc.contributor.author | Rayas-Sánchez, José E. | - |
dc.contributor.author | Zhang, Qi-Jun | - |
dc.contributor.author | Rautio, James C. | - |
dc.contributor.author | Nikolova, Natalia K. | - |
dc.contributor.author | Boria, Vicente E. | - |
dc.contributor.author | Cheng, Qingsha S. | - |
dc.contributor.author | Yu, Ming | - |
dc.contributor.author | Hoefer, Wolfgang J.R. | - |
dc.date.accessioned | 2024-09-05T18:21:07Z | - |
dc.date.available | 2024-09-05T18:21:07Z | - |
dc.date.issued | 2024-08-12 | - |
dc.identifier.citation | J. E. Rayas-Sánchez et al., "Microwave Modeling and Design Optimization: The Legacy of John Bandler," in IEEE Transactions on Microwave Theory and Techniques, early access, Aug. 12, 2024, doi: 10.1109/TMTT.2024.3437198. | en_US |
dc.identifier.issn | 0018-9480 | - |
dc.identifier.other | 10.1109/TMTT.2024.3437198 | - |
dc.identifier.uri | http://hdl.handle.net/11375/30128 | - |
dc.description | Article regarding Dr. Bandler's contributions to the RF and microwave engineering field. An expanded version of the material was presented by the authors at the memorial session honoring Dr. Bandler during the IEEE MTT-S International Microwave Symposium (IMS) in Washington D.C., USA, June 16-21, 2024. | en_US |
dc.description.abstract | In this article, we honor Prof. John W. Bandler and his legacy in RF and microwave modeling and automated design optimization. We showcase his pioneering breakthroughs in minimax optimization, p th norm formulations, yield optimization, and nonlinear circuit design optimization. We highlight advances in direct electromagnetic (EM) microwave optimization, circuit response sensitivities, and efficient S -parameters sensitivity calculations. We explore the port-tuning version of space mapping (SM) for EM-based analysis, techniques for industrial microwave design of satellite systems, and post-manufacture hardware tuning. The integration of artificial neural networks (ANNs) with SM for enhanced EM-based design optimization and yield prediction, cognition-driven microwave filter design, and parallels between SM and artificial intelligence (AI) is examined. Finally, we speculate on the future integration of cognitive science with engineering design, leveraging the synergy of AI, machine learning (ML), and SM. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE Transactions on Microwave Theory and Techniques (Early Access) | en_US |
dc.subject | microwave filters | en_US |
dc.subject | microwave theory and techniques | en_US |
dc.subject | microwave circuits | en_US |
dc.subject | optimization | en_US |
dc.subject | integrated circuit modeling | en_US |
dc.subject | circuits | en_US |
dc.subject | adjoint sensitivities | en_US |
dc.subject | artificial intelligence (AI) | en_US |
dc.subject | circuit optimization | en_US |
dc.subject | electromagnetic (EM) optimization | en_US |
dc.subject | design centering | en_US |
dc.subject | design optimization | en_US |
dc.subject | frequency scaling | en_US |
dc.subject | machine learning (ML) | en_US |
dc.subject | minimax | en_US |
dc.subject | neural networks | en_US |
dc.subject | parameter extraction | en_US |
dc.subject | port tuning | en_US |
dc.subject | sensitivities | en_US |
dc.subject | space mapping (SM) | en_US |
dc.subject | surrogate modeling | en_US |
dc.subject | statistical analysis | en_US |
dc.subject | yield | en_US |
dc.title | Microwave Modeling and Design Optimization: The Legacy of John Bandler | en_US |
dc.type | Preprint | en_US |
dc.contributor.department | Electrical and Computer Engineering | en_US |
Appears in Collections: | John Bandler Publications |
Files in This Item:
File | Description | Size | Format | |
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Microwave_Modeling_and_Design_Optimization_The_Legacy_of_John_Bandler.pdf | 2.73 MB | Adobe PDF | View/Open |
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