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Anna Pietrenko-Dabrowska

Bio: Anna Pietrenko-Dabrowska is an academic researcher from Gdańsk University of Technology. The author has contributed to research in topics: Antenna (radio) & Computer science. The author has an hindex of 9, co-authored 108 publications receiving 337 citations.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: A novel technique for surrogate modeling of antenna structures is proposed that involves a construction of two levels of surrogates, both realized as kriging interpolation models and allows uniform allocation of training data samples in a straightforward manner.
Abstract: Utilization of electromagnetic (EM) simulation tools is mandatory in the design of contemporary antenna structures. At the same time, conducting design procedures that require multiple evaluations of the antenna at hand, such as parametric optimization or yield-driven design, is hindered due to the high cost of accurate EM analysis. To a certain extent, this issue can be addressed using fast replacement models (also referred to as surrogates). Unfortunately, due to curse of dimensionality, traditional data-driven surrogate modeling methods are limited to antenna structures described by a few parameters with relatively narrow parameter ranges. This is by no means sufficient given the complexity of modern designs. In this paper, a novel technique for surrogate modeling of antenna structures is proposed. It involves a construction of two levels of surrogates, both realized as kriging interpolation models. The first model is based on a set of reference designs optimized for selected performance figures. It is used to establish a domain for the final (second level) surrogate. This formulation permits efficient modeling within wide ranges of antenna geometry parameters and wide ranges of performance figures (e.g., operating frequencies). At the same time, it allows uniform allocation of training data samples in a straightforward manner. Our approach is demonstrated using two microstrip antenna examples and is compared with conventional kriging and radial basis function modeling. Application examples for antenna optimization are also provided along with experimental validation.

93 citations

Journal ArticleDOI
TL;DR: This work proposes a reduced cost trust-region algorithm with sparse updates of the antenna response Jacobian decided based on relocation of the design variable vector between algorithm iterations and the update history, which permits significant reduction of the optimisation cost without affecting the design quality in a significant manner.
Abstract: Numerical optimisation plays more and more important role in the antenna design. Because of lack of design-ready theoretical models, electromagnetic (EM)-simulation-driven adjustment of geometry parameters is a necessary step of the design process. At the same time, traditional parameter sweeping cannot handle complex topologies and large number of design variables. On the other hand, high computational cost of the conventional optimisation routines can be reduced using, e.g., surrogate-assisted techniques. Still, direct optimisation of EM simulation antenna models is required at certain level of fidelity. This work proposes a reduced cost trust-region algorithm with sparse updates of the antenna response Jacobian, decided based on relocation of the design variable vector between algorithm iterations and the update history. Our approach permits significant reduction of the optimisation cost (∼40% as compared to the reference algorithm) without affecting the design quality in a significant manner. Robustness of the proposed technique is validated using a set of benchmark antennas, statistical analysis of the algorithm performance over multiple initial designs, as well as investigating the effects of its control parameters that permit control efficiency vs. design quality trade-off. Selected designs were fabricated and measured to validate the computational models utilised in the optimisation process.

56 citations

Journal ArticleDOI
TL;DR: This survey provides an overview of recent techniques and technologies investigated in the literature, to implement high performance on-chip antennas for millimeter-waves (mmWave) and terahertz (THz) integrated-circuit (IC) applications.
Abstract: Antennas on-chip are a particular type of radiating elements valued for their small footprint. They are most commonly integrated in circuit boards to electromagnetically interface free space, which is necessary for wireless communications. Antennas on-chip radiate and receive electromagnetic (EM) energy as any conventional antennas, but what distinguishes them is their miniaturized size. This means they can be integrated inside electronic devices. Although on-chip antennas have a limited range, they are suitable for cell phones, tablet computers, headsets, global positioning system (GPS) devices, and WiFi and WLAN routers. Typically, on-chip antennas are handicapped by narrow bandwidth (less than 10%) and low radiation efficiency. This survey provides an overview of recent techniques and technologies investigated in the literature, to implement high performance on-chip antennas for millimeter-waves (mmWave) and terahertz (THz) integrated-circuit (IC) applications. The technologies discussed here include metamaterial (MTM), metasurface (MTS), and substrate integrated waveguides (SIW). The antenna designs described here are implemented on various substrate layers such as Silicon, Graphene, Polyimide, and GaAs to facilitate integration on ICs. Some of the antennas described here employ innovative excitation mechanisms, for example comprising open-circuited microstrip-line that is electromagnetically coupled to radiating elements through narrow dielectric slots. This excitation mechanism is shown to suppress surface wave propagation and reduce substrate loss. Other techniques described like SIW are shown to significantly attenuate surface waves and minimise loss. Radiation elements based on the MTM and MTS inspired technologies are shown to extend the effective aperture of the antenna without compromising the antenna’s form factor. Moreover, the on-chip antennas designed using the above technologies exhibit significantly improved impedance match, bandwidth, gain and radiation efficiency compared to previously used technologies. These features make such antennas a prime candidate for mmWave and THz on-chip integration. This review provides a thorough reference source for specialist antenna designers.

48 citations

Journal ArticleDOI
TL;DR: This paper proposes a simple technique for rapid surrogate-assisted yield optimization of narrow- and multi-band antennas by considering a few pre-optimized designs that represent the directions of the major changes of the antenna resonant frequencies and operating bands.
Abstract: Uncertainty quantification is an important aspect of engineering design, also pertaining to the development and performance evaluation of antenna systems. Manufacturing tolerances as well as other types of uncertainties, related to material parameters (e.g., substrate permittivity) or operating conditions (e.g., bending) may affect the antenna characteristics. In the case of narrow- or multi-band antennas, this usually leads to frequency shifts of the operating bands. Quantifying these effects is imperative to adequately assess the design quality, either in terms of the statistical moments of the performance parameters or the yield. Reducing the antenna sensitivity to parameter deviations is even more essential when increasing the probability of the system satisfying the prescribed requirements is of concern. The prerequisite of such procedures is statistical analysis, normally carried out at the level of full-wave electromagnetic (EM) analysis. While necessary to ensure reliability, it entails considerable computational expenses, often prohibitive. Following the recently fostered concept of constrained modeling, this paper proposes a simple technique for rapid surrogate-assisted yield optimization of narrow- and multi-band antennas. The keystone of the approach is an appropriate definition of the optimization domain. This is realized by considering a few pre-optimized designs that represent the directions of the major changes of the antenna resonant frequencies and operating bands. Due to a small volume of such a domain, an accurate replacement model can be established therein using a small number of training samples, and employed to improve the antenna yield. Verification results obtained for a ring-slot antenna, a dual-band and a triple-band uniplanar dipoles indicate that the optimization process can be accomplished at low cost of a few dozen of EM simulations: 62, 74 and 132 EM simulations, respectively. Result reliability is validated through comparisons with EM-based Monte Carlo simulations.

48 citations

Journal ArticleDOI
TL;DR: This study proposes an efficient gradient search algorithm with numerical derivatives that is competitive to both the reference trust-region algorithm as well as its recently reported accelerated versions.
Abstract: Electromagnetic (EM) simulation tools are of primary importance in the design of contemporary antennas. The necessity of accurate performance evaluation of complex structures is a reason why the final tuning of antenna dimensions, aimed at improvement of electrical and field characteristics, needs to be based on EM analysis. Design automation is highly desirable and can be achieved by coupling EM solvers with numerical optimisation routines. Unfortunately, its computational overhead may be impractically high for conventional algorithms. This study proposes an efficient gradient search algorithm with numerical derivatives. The acceleration of the optimisation process is obtained by means of the two mechanisms developed to suppress some of finite-differentiation-based updates of the antenna response sensitivities that involve monitoring and quantifying the gradient changes as well as design relocation between the consecutive algorithm iterations. Both methods considerably reduce the need for finite differentiation, leading to significant computational savings. At the same time, excellent reliability and repeatability is maintained, which is demonstrated through statistics over multiple algorithm runs with random initial designs. The proposed approach is validated using a benchmark set of wideband antennas. The proposed algorithm is competitive to both the reference trust-region algorithm as well as its recently reported accelerated versions.

42 citations


Cited by
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Journal ArticleDOI
TL;DR: The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.
Abstract: In recent years, the research community has witnessed an explosion of literature dealing with the mimicking of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.

401 citations

01 Jan 2006
TL;DR: In this paper, an improved version of space mapping, manifold mapping, is proposed to find a precise solution with the same computational efficiency. But the manifold mapping solution does not always coincide with the accurate model optimum.
Abstract: Optimization procedures in practice are based on highly accurate models that typically have an excessive computational cost. By exploiting auxiliary models that are less accurate but much cheaper to compute, space-mapping has been reported to accelerate such procedures. However, the space-mapping solution does not always coincide with the accurate model optimum. We introduce manifold mapping, an improved version of space mapping that finds this precise solution with the same computational efficiency. By an example in linear actuator design we show that our technique delivers a significant speed-up compared to other optimization schemes

72 citations

Journal ArticleDOI
11 Jan 2021
TL;DR: In this article, the authors 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, including reliable and computationally efficient optimization of highly accurate system-level complex models subject to statistical uncertainty and varying operating or environmental conditions.
Abstract: 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.

55 citations

Journal ArticleDOI
TL;DR: A systematic literature review of metamodeling-based simulation optimization (MBSO) suggests that this research area is growing in the past 15 years.

49 citations

Journal ArticleDOI
TL;DR: This paper proposes a simple technique for rapid surrogate-assisted yield optimization of narrow- and multi-band antennas by considering a few pre-optimized designs that represent the directions of the major changes of the antenna resonant frequencies and operating bands.
Abstract: Uncertainty quantification is an important aspect of engineering design, also pertaining to the development and performance evaluation of antenna systems. Manufacturing tolerances as well as other types of uncertainties, related to material parameters (e.g., substrate permittivity) or operating conditions (e.g., bending) may affect the antenna characteristics. In the case of narrow- or multi-band antennas, this usually leads to frequency shifts of the operating bands. Quantifying these effects is imperative to adequately assess the design quality, either in terms of the statistical moments of the performance parameters or the yield. Reducing the antenna sensitivity to parameter deviations is even more essential when increasing the probability of the system satisfying the prescribed requirements is of concern. The prerequisite of such procedures is statistical analysis, normally carried out at the level of full-wave electromagnetic (EM) analysis. While necessary to ensure reliability, it entails considerable computational expenses, often prohibitive. Following the recently fostered concept of constrained modeling, this paper proposes a simple technique for rapid surrogate-assisted yield optimization of narrow- and multi-band antennas. The keystone of the approach is an appropriate definition of the optimization domain. This is realized by considering a few pre-optimized designs that represent the directions of the major changes of the antenna resonant frequencies and operating bands. Due to a small volume of such a domain, an accurate replacement model can be established therein using a small number of training samples, and employed to improve the antenna yield. Verification results obtained for a ring-slot antenna, a dual-band and a triple-band uniplanar dipoles indicate that the optimization process can be accomplished at low cost of a few dozen of EM simulations: 62, 74 and 132 EM simulations, respectively. Result reliability is validated through comparisons with EM-based Monte Carlo simulations.

48 citations