| dc.contributor.author | Rayas-Sánchez, José E. | |
| dc.contributor.author | Koziel, Slawomir | |
| dc.contributor.author | Bandler, John W. | |
| dc.date.accessioned | 2021-02-12T01:50:27Z | |
| dc.date.accessioned | 2023-03-21T19:29:39Z | |
| dc.date.available | 2021-02-12T01:50:27Z | |
| dc.date.available | 2023-03-21T19:29:39Z | |
| dc.date.issued | 2021-01-11 | |
| dc.identifier.citation | J. E. Rayas-Sánchez, S. Koziel, and J. W. Bandler, “Advanced RF and microwave design optimization: a journey and a vision of future trends,” IEEE J. of Microwaves, vol. 1, no. 1, pp. 481-493, Jan. 2021. (p-ISSN: 2692-8388; published online: 11 Jan. 2021; DOI: 10.1109/JMW.2020.3034263) | es_MX |
| dc.identifier.issn | 2692-8388 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12032/74734 | |
| dc.description | In this paper, we outline the historical evolution of RF and microwave design optimization and envisage imminent and future challenges that will be addressed by the next generation of optimization developments. Our journey starts in the 1960s, with the emergence of formal numerical optimization algorithms for circuit design. In our fast historical analysis, we emphasize the last two decades of documented microwave design optimization problems and solutions. From that retrospective, we identify a number of prominent scientific and engineering challenges: 1) the reliable and computationally efficient optimization of highly accurate system-level complex models subject to statistical uncertainty and varying operating or environmental conditions; 2) the computationally-efficient EM-driven multi-objective design optimization in high-dimensional design spaces including categorical, conditional, or combinatorial variables; and 3) the manufacturability assessment, statistical design, and yield optimization of high-frequency structures based on high-fidelity multi-physical representations. To address these major challenges, we venture into the development of sophisticated optimization approaches, exploiting confined and dimensionally reduced surrogate vehicles, automated feature-engineering-based optimization, and formal cognition-driven space mapping approaches, assisted by Bayesian and machine learning techniques. | es_MX |
| dc.description.sponsorship | ITESO, A.C. | es_MX |
| dc.language.iso | eng | es_MX |
| dc.publisher | IEEE | es_MX |
| dc.rights.uri | http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-ND-2.5-MX.pdf | es_MX |
| dc.subject | ANN | es_MX |
| dc.subject | Bayesian | es_MX |
| dc.subject | Broyden | es_MX |
| dc.subject | CAD | es_MX |
| dc.subject | RF and Microwave Design Optimization | es_MX |
| dc.subject | Design Automation | es_MX |
| dc.subject | Gaussian Process | es_MX |
| dc.subject | Machine Learning | es_MX |
| dc.title | Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends | es_MX |
| dc.type | info:eu-repo/semantics/article | es_MX |
