Infinitesimal Dipole Model Using Space Mapping Optimization for Antenna Placement

Pietrenko‐Dabrowska

2020

Electromagnetic (EM) simulations have become an indispensable tool in the design of contemporary antennas. EM-driven tasks, for example, parametric optimization, entail considerable computational efforts, which may be reduced by employing surrogate models. Yet, data-driven modelling of antenna characteristics is largely hindered by the curse of dimensionality. This may be addressed using the recently reported domain-confinement techniques, especially the nested-kriging framework, which permits rendering of reliable surrogates over wide ranges of antenna parameters while greatly reducing the computational overhead of training data acquisition. Focused on modelling of multi-band antennas, this paper attempts to reduce the cost of surrogate construction even further by incorporating variable-fidelity simulations into the nested kriging. The principal challenge being design-dependent frequency shifts between the models of various fidelities is handled through the development of a customized frequency scaling and output space mapping. Validation is carried out using a dual-band dipole antenna modeled over broad ranges of operating conditions. A small training data set is sufficient to secure the predictive power comparable to that of the nested kriging model set up using solely high-fidelity data, and by far exceeding the accuracy of conventional surrogates. Application examples for antenna optimization and experimental verification of the selected designs are also provided.

Section: Introductionmentioning

confidence: 99%

Cost‐efficient performance‐driven modelling of multi‐band antennas by variable‐fidelity electromagnetic simulations and customized space mapping

Pietrenko‐Dabrowska

2020

“…13 The necessity of extending the computational domain through incorporation of connectors, radomes, feeding structures, or other (coupled) radiators, only aggravates the problem. A number of techniques have been developed to improve the computational efficiency of simulation-based procedures, including adjoints sensitivities, 14,15 gradient-based search with sparse sensitivity updates, [16][17][18] machine learning methods, 19,20 as well as surrogate-assisted frameworks (space mapping, 21,22 response correction techniques, 23,24 and feature-based optimization 25 ).…”

Section: Introductionmentioning

confidence: 99%

Variable‐fidelity modeling of antenna input characteristics using domain confinement and two‐stage Gaussian process regression surrogates

Jacobs

2020

“…The difficulties outlined in the previous paragraph can be—to a certain extent—overcome by fast replacement models (surrogates). Surrogates can be roughly divided into approximation (or data‐driven) ones and physics‐based ones . The first group is far more popular because of a conceptual simplicity and wide accessibility of relevant computer codes.…”

Section: Introductionmentioning

confidence: 99%

Enhanced uniform data sampling for constrained data‐driven modeling of antenna input characteristics

Sigurðsson

Pietrenko‐Dabrowska

et al. 2019

Data-driven surrogates are the most popular replacement models utilized in many fields of engineering and science, including design of microwave and antenna structures. The primary practical issue is a curse of dimensionality, which limits the number of independent parameters that can be accounted for in the modeling process. Recently, a performance-driven modeling technique has been proposed where the constrained domain of the model is spanned by a set of reference designs optimized with respect to selected figures of interest. This approach allows for significant improvement of prediction power of the surrogates without the necessity of reducing the parameter ranges. Yet uniform allocation of the training data samples in the constrained domain remains a problem. Here, a novel design of experiments technique ensuring better sample uniformity is proposed. Our approach involves uniform sampling on the domain-spanning manifold and linear transformation of the remaining sample vector components onto orthogonal directions with respect to the manifold. Two antenna examples are provided to demonstrate the advantages of the technique, including application case studies (antenna optimization). KEYWORDS antenna design, constrained modeling, data-driven modeling, design of experiments, simulationbased design, uniform sampling 1 | INTRODUCTIONThe most versatile and ubiquitous antenna design tools nowadays are full-wave electromagnetic (EM) simulators. EM analysis permits reliable performance evaluation when executed at sufficient discretization level of the structure. EMdriven design closure (primarily, adjustment of geometry parameters) is mandatory yet challenging stage of the design process. The primary problem is a high cost of simulation, which may be acceptable for simple designs but not so much for complex structures described by a large number of parameters. In particular, numerous evaluations required by, eg, conventional optimization algorithms, 1-5 may be impractical. The problem is even more pronounced for tasks involving massive simulations such as statistical analysis 6 or tolerance-aware design. 7,8 The most common work-around is an interactive design based on parameter sweeping; however, this approach has serious limitations: it fails to yield optimum designs, cannot handle design constraints, or cannot account for parameter interactions, to name just a few.