首页|Reykjavik University Researchers Detail Research in Machine Learning (Machine-le arning-based global optimization of microwave passives with variable-fidelity EM models and response features)
Reykjavik University Researchers Detail Research in Machine Learning (Machine-le arning-based global optimization of microwave passives with variable-fidelity EM models and response features)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news originating from Reykjavik University by New sRx correspondents,research stated,"Maximizing microwave passive component per formance demands precise parameter tuning,particularly as modern circuits grow increasingly intricate. Yet,achieving this often requires a comprehensive appro ach due to their complex geometries and miniaturized structures." Financial supporters for this research include Icelandic Centre For Research; Na rodowe Centrum Nauki. Our news journalists obtained a quote from the research from Reykjavik Universit y: "However,the computational burden of optimizing these components via full-wa ve electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit reliability,but the expense of conducting rudimentary EM-driven glo bal optimization by means of popular bio-inspired algorithms is impractical. Sim ilarly,nonlinear system characteristics pose challenges for surrogate-assisted methods. This paper introduces an innovative technique leveraging variable-fidel ity EM simulations and response feature technology within a kriging-based machin e-learning framework for cost-effective global parameter tuning of microwave pas sives. The efficiency of this approach stems from performing most operations at the low-fidelity simulation level and regularizing the objective function landsc ape through the response feature method. The primary prediction tool is a co-kri ging surrogate,while a particle swarm optimizer,guided by predicted objective function improvements,handles the search process."
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