Antenna optimization using machine learning with reduced-dimensionality surrogates.

In modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak performance. Often, global adjustments to geometry are necessary for optimal results.

Improved efficacy behavioral modeling of microwave circuits through dimensionality reduction and fast global sensitivity analysis.

Behavioral models have garnered significant interest in the realm of high-frequency electronics. Their primary function is to substitute costly computational tools, notably electromagnetic (EM) analysis, for repetitive evaluations of the structure under consideration. These evaluations are often necessary for tasks like parameter tuning, statistical analysis, or multi-criterial design.

Novel versatile topologies and design optimization of wide-bandstop frequency selective surfaces for X-band, Ku-band and millimeter-wave applications.

Novel designs of frequency selective surface (FSS) are presented for wideband applications in X, Ku and mmWave (millimeter Wave) bands. Two identical metallic layers of FSS are imprinted on both sides of the RO4003 substrate. The geometry parameters are optimized to maximize the bandstop at the specified in-band maximum transmission level of -10 dB; satisfaction of the latter condition is enforced through appropriate formulation and handling of the design constraints.