Rayas-Sánchez, José E.Zhang, Qi-JunRautio, James C.Nikolova, Natalia K.Boria, Vicente E.Cheng, Qingsha S.Yu, MingHoefer, Wolfgang J. R.2025-01-102025-01-102024-08-12J. E. Rayas-Sánchez, Q. J. Zhang, J. W. Rautio, N. K. Nikolova, V. E. Boria, Q. S. Cheng, M. Yu, and W. J. R. Hoefer, “Microwave modeling and design optimization: The legacy of John Bandler,” IEEE Trans. Microwave Theory Techn., vol. 73, no. 01, pp. 87-101, Jan. 2025. (p-ISSN: 0018-9480; e-ISSN: 1557-9670; published online: 12 August 2024; DOI: 10.1109/TMTT.2024.3437198)0018-9480https://hdl.handle.net/11117/11369In this paper we honor Professor 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 EM microwave optimization, circuit response sensitivities, and efficient S-parameters sensitivity calculations. We explore the port-tuning version of space mapping for EM-based analysis, techniques for industrial microwave design of satellite systems, and post-manufacture hardware tuning. The integration of artificial neural networks with space mapping for enhanced EM-based design optimization and yield prediction, cognition-driven microwave filter design, and parallels between space mapping and artificial intelligence (AI) are examined. Finally, we speculate on the future integration of cognitive science with engineering design, leveraging the synergy of AI, machine learning, and space mapping.engAdjoint sensitivitiesartificial intelligencecircuit optimizationEM optimizationdesign centeringdesign optimizationfrequency scalingmachine learningmicrowave circuitsminimaxneural networksparameter extractionport tuningsensitivitiesspace mappingsurrogate modelingstatistical analysisyieldAdjoint sensitivitiesyieldinfo:eu-repo/semantics/article