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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/30128
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dc.contributor.authorRayas-Sánchez, José E.-
dc.contributor.authorZhang, Qi-Jun-
dc.contributor.authorRautio, James C.-
dc.contributor.authorNikolova, Natalia K.-
dc.contributor.authorBoria, Vicente E.-
dc.contributor.authorCheng, Qingsha S.-
dc.contributor.authorYu, Ming-
dc.contributor.authorHoefer, Wolfgang J.R.-
dc.date.accessioned2024-09-05T18:21:07Z-
dc.date.available2024-09-05T18:21:07Z-
dc.date.issued2024-08-12-
dc.identifier.citationJ. 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.issn0018-9480-
dc.identifier.other10.1109/TMTT.2024.3437198-
dc.identifier.urihttp://hdl.handle.net/11375/30128-
dc.descriptionArticle 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.abstractIn 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.isoenen_US
dc.publisherIEEE Transactions on Microwave Theory and Techniques (Early Access)en_US
dc.subjectmicrowave filtersen_US
dc.subjectmicrowave theory and techniquesen_US
dc.subjectmicrowave circuitsen_US
dc.subjectoptimizationen_US
dc.subjectintegrated circuit modelingen_US
dc.subjectcircuitsen_US
dc.subjectadjoint sensitivitiesen_US
dc.subjectartificial intelligence (AI)en_US
dc.subjectcircuit optimizationen_US
dc.subjectelectromagnetic (EM) optimizationen_US
dc.subjectdesign centeringen_US
dc.subjectdesign optimizationen_US
dc.subjectfrequency scalingen_US
dc.subjectmachine learning (ML)en_US
dc.subjectminimaxen_US
dc.subjectneural networksen_US
dc.subjectparameter extractionen_US
dc.subjectport tuningen_US
dc.subjectsensitivitiesen_US
dc.subjectspace mapping (SM)en_US
dc.subjectsurrogate modelingen_US
dc.subjectstatistical analysisen_US
dc.subjectyielden_US
dc.titleMicrowave Modeling and Design Optimization: The Legacy of John Bandleren_US
dc.typePreprinten_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
Appears in Collections:John Bandler Publications

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