Inverse design of EBG waveguides through scattering matrices

Palmeri, Roberta; Isernia, Tommaso

doi:10.1051/epjam/2020009

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Metamaterials do not escape the need for optimization [15,17,18], in particular with the prowess achieved by artificial intelligence, in the broadest sense, and more precisely by neural networks [18]. Actually, only a few papers deal with the application of neural networks to metamaterials.…”

Section: Introductionmentioning

confidence: 99%

Morphing for faster computations with finite difference time domain algorithms

et al. 2022

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In the framework of wave propagation, finite difference time domain (FDTD) algorithms, yield high computational time. We propose to use morphing algorithms to deduce some approximate wave pictures of their interactions with fluid-solid structures of various shapes and different sizes deduced from FDTD computations of scattering by solids of three given shapes: triangular, circular and elliptic ones. The error in the L2 norm between the FDTD solution and approximate solution deduced via morphing from the source and destination images are typically less than 1% if control points are judiciously chosen. We thus propose to use a morphing algorithm to deduce approximate wave pictures: at intermediate time steps from the FDTD computation of wave pictures at a time step before and after this event, and at the same time step, but for an average frequency signal between FDTD computation of wave pictures with two different signal frequencies. We stress that our approach might greatly accelerate FDTD computations as discretizations in space and time are inherently linked via the Courant–Friedrichs–Lewy stability condition. Our approach requires some human intervention since the accuracy of morphing highly depends upon control points, but compared to the direct computational method our approach is much faster and requires fewer resources. We also compared our approach to some neural style transfer (NST) algorithm, which is an image transformation method based on a neural network. Our approach outperforms NST in terms of the L2 norm, Mean Structural SIMilarity, expected signal to error ratio.

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Section: Introductionmentioning

confidence: 99%

Morphing for faster computations with finite difference time domain algorithms

et al. 2022

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Learning-Based Procedures for Inverse Design of Electromagnetic Devices: A Preliminary Investigation

Palmeri,

Ruiz,

Scapaticci

et al. 2024

2024 18th European Conference on Antennas and Propagation (EuCAP)

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A Hybrid Multi-Objective Optimization Method and Its Application to Electromagnetic Device Designs

Zhengwei

Yang

2022

Applied Sciences

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Optimization algorithms play a critical role in electromagnetic device designs due to the ever-increasing technological and economical competition. Although evolutionary algorithm-based methods have successfully been applied to different design problems, these methods exhibit deficiencies when solving complex problems with multimodal and discontinuous objective functions, which is quite common in electromagnetic device optimization designs. In this paper, a hybrid multi-objective optimization algorithm based on a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective particle swarm optimization method (MOPSO) is proposed. In order to enhance the convergence and diversity performance of the algorithm, a new population update mechanism of MOPSO is introduced. Moreover, an adaptive operator involving crossover and mutation is presented to achieve a better balance between global and local searches. The performance of the hybrid algorithm is validated using standard test functions and the multi-objective design of a superconducting magnetic energy storage (SMES) device. Numerical results demonstrate the effectiveness and superiority of the proposed method.

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Inverse design of EBG waveguides through scattering matrices

Cited by 3 publications

References 27 publications

Morphing for faster computations with finite difference time domain algorithms

Morphing for faster computations with finite difference time domain algorithms

Learning-Based Procedures for Inverse Design of Electromagnetic Devices: A Preliminary Investigation

A Hybrid Multi-Objective Optimization Method and Its Application to Electromagnetic Device Designs

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