Multifeature-Assisted Neuro-transfer Function Surrogate-Based EM Optimization Exploiting Trust-Region Algorithms for Microwave Filter Design

Feng, Feng; Na, Weicong; Liu, Wenyuan; Yan, Shuxia; Zhu, Lin; Ma, Jan; Zhang, Qi‐Jun

doi:10.1109/tmtt.2019.2952101

Cited by 69 publications

(47 citation statements)

References 39 publications

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“…The applications include automated determination of the number of state variables in Wiener‐type model 10 for nonlinear microwave device modeling, automated modeling with dynamic space mapping 36 where the most suitable order for a dynamic neural network model is automatically selected, and automated modeling with recurrent neural network 57 for nonlinear behavioral modeling. As further research directions, the extrapolation techniques can be applied to parametric modeling of multiphysics problems 58,59 and surrogate‐based EM optimization 60,61 to further improve the stability of microwave component design.…”

Section: Discussionmentioning

confidence: 99%

Recent advances in knowledge‐based model structure optimization and extrapolation techniques for microwave applications

Yan

Feng

et al. 2021

Int J Numerical Modelling

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Artificial neural network modeling techniques have been recognized as important vehicles in the microwave computer‐aided design (CAD) area in addressing the growing challenges of designing next generation microwave device, circuits, and systems. This article provides an overview of recent advances in knowledge‐based neural network model generation and extrapolation techniques for microwave applications. We first introduce the unified knowledge‐based neural network structure optimization technique. Using the distinctive property for feature selection of l1 optimization, this unified modeling technique efficiently determines the type and topology of the mapping structure in a knowledge‐based model. This knowledge‐based model structure optimization technique is more flexible and systematic, and can further speed up the knowledge‐based neural model development. As a further advancement, we also discuss the advanced multi‐dimensional extrapolation technique for neural‐based microwave modeling. The purpose is to make the neural network model can be reliably used not only inside the training range but also outside the training range. Multi‐dimensional cubic polynomial extrapolation formulation and optimization over grids outside the training range are utilized to make neural models more robust and reliable when they are used outside the training range. The validity of these techniques is demonstrated by microwave modeling examples.

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Section: Discussionmentioning

confidence: 99%

Recent advances in knowledge‐based model structure optimization and extrapolation techniques for microwave applications

Yan

Feng

et al. 2021

Int J Numerical Modelling

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“…However, direct EM optimization of metasurface designs when using conventional algorithms may be prohibitively expensive, especially when global search is required. A practical workaround is utilization of machine learning methods [35]- [39], including surrogate modeling techniques [41]- [44]. Shifting the computational burden to a cheaper representation of the structure at hand and the incorporation of other means such as problem decomposition [45] may expedite the parameter tuning process and enable globalized search, otherwise infeasible when operating directly on EM simulation models.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Powered EM-Based Framework for Efficient and Reliable Design of Low Scattering Metasurfaces

Koziel

Abdullah

2021

IEEE Trans. Microwave Theory Techn.

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Popularity of metasurfaces has been continuouslygrowing due to their attractive properties including the ability to effectively manipulate electromagnetic (EM) waves. Metasurfaces comprise optimized geometries of unit cells arranged as a periodic lattice to obtain a desired EM response. One of their emerging application areas is the stealth technology, in particular, realization of radar cross section (RCS) reduction. Despite potential benefits, a practical obstacle hindering widespread metasurface utilization is the lack of systematic design procedures. Conventional approaches are largely intuitioninspired and demand heavy designer's interaction while exploring the parameter space and pursuing optimum unit cell geometries. Not surprisingly, these are unable to identify truly optimum solutions. In this article, we introduce a novel machine-learningbased framework for automated and computationally efficient design of metasurfaces realizing broadband RCS reduction. Our methodology is a three-stage procedure that involves global surrogate-assisted optimization of the unit cells, followed by their local refinement. The last stage is direct EM-driven maximization of the RCS reduction bandwidth, facilitated by appropriate formulation of the objective function involving regularization terms. The appealing feature of the proposed framework is that it optimizes the RCS reduction bandwidth directly at the level of the entire metasurface as opposed to merely optimizing unit cell geometries. Computational feasibility of the optimization process, especially its last stage, is ensured by high-quality initial designs rendered during the first two stages. To corroborate the utility of our procedure, it has been applied to several metasurface designs reported in the literature, leading to the RCS reduction bandwidth improvement by 15%-25% when compared with the original designs. Furthermore, it was used to design a novel metasurface featuring over 100% of relative bandwidth. Although the procedure has been used in the context of RCS design, it can be generalized to handle metasurface development for other application areas.

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“…Although numerical optimization seems to be appropriate for addressing the difficulties pertinent to parameter adjustments, it suffers from high cost related to a large number of CPU-heavy evaluations required by the algorithm to converge. Consequently, maintaining low computational budget—e.g., using surrogate methods [ 40 , 41 , 42 ]—is of high importance in increasing the usefulness of algorithm-based approaches to BM design. The motivation of this work is to address the discussed challenges pertinent to design of modern BM circuits.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Unattended Design of Miniaturized 4 × 4 Butler Matrices with Nonstandard Phase Differences

Bekasiewicz

Koziel

2021

Sensors

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Design of Butler matrices dedicated to Internet of Things and 5th generation (5G) mobile systems—where small size and high performance are of primary concern—is a challenging task that often exceeds capabilities of conventional techniques. Lack of appropriate, unified design approaches is a serious bottleneck for the development of Butler structures for contemporary applications. In this work, a low-cost bottom-up procedure for rigorous and unattended design of miniaturized 4 × 4 Butler matrices is proposed. The presented approach exploits numerical algorithms (governed by a set of suitable objective functions) to control synthesis, implementation, optimization, and fine-tuning of the structure and its individual building blocks. The framework is demonstrated using two miniaturized matrices with nonstandard output-port phase differences. Numerical results indicate that the computational cost of the design process using the presented framework is over 80% lower compared to the conventional approach. The footprints of optimized matrices are only 696 and 767 mm2, respectively. Small size and operation frequency of around 2.6 GHz make the circuits of potential use for mobile devices dedicated to work within a sub-6 GHz 5G spectrum. Both structures have been benchmarked against the state-of-the-art designs from the literature in terms of performance and size. Measurements of the fabricated Butler matrix prototype are also provided.

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Multifeature-Assisted Neuro-transfer Function Surrogate-Based EM Optimization Exploiting Trust-Region Algorithms for Microwave Filter Design

Cited by 69 publications

References 39 publications

Recent advances in knowledge‐based model structure optimization and extrapolation techniques for microwave applications

Recent advances in knowledge‐based model structure optimization and extrapolation techniques for microwave applications

Machine-Learning-Powered EM-Based Framework for Efficient and Reliable Design of Low Scattering Metasurfaces

Low-Cost Unattended Design of Miniaturized 4 × 4 Butler Matrices with Nonstandard Phase Differences

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