A computational approach for engineering optical properties of multilayer thin films: Particle swarm optimization applied to Bruggeman homogenization formalism

Siabi-Garjan, Araz; Hassanzadeh, Reza

doi:10.1140/epjp/i2018-12203-y

Cited by 11 publications

(3 citation statements)

References 28 publications

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“…The configurational flexibility of ZALs can open new opportunities in the field of microcavities or low threshold laser sources. Note that, in the homogenization limit, we expect the fluctuations in the film properties may stimulate similar effect as in the case of effective medium approximation for various particle inclusion scenarios as reported in [56] using particle swarm optimization applied to Bruggeman homogenization formalism.…”

Section: Introductionsupporting

confidence: 59%

Sensitivity of resonance properties of all-dielectric multilayers driven by statistical fluctuations

Lereu¹,

Lemarchand²,

Zerrad³

et al. 2019

Opt. Express

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In photonics and emerging fields of quantum and topological materials, increasing demands are placed upon the state and control of electromagnetic fields. Dielectric multilayer materials may be designed and optimized to possess extremely sharp spectral and angular photonic resonances allowing for the creation of fields orders of magnitude larger than the exciting field. With enhancements of 10 4 and higher, the extreme nature of these resonances places high constraints on the statistical properties of the physical and optical characteristics of the materials. To what extent the spectral and angular shifts occur as a result of fluctuations in the refractive indices and morphologies of the involved low-loss subdomains have not been considered previously. Here, we present how parameter variations such as those caused by fluctuations in deposition rate, yielding bias, random and compensated errors, may affect the resonance properties of low-loss all-dielectric stacks.

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Section: Introductionsupporting

confidence: 59%

Sensitivity of resonance properties of all-dielectric multilayers driven by statistical fluctuations

Lereu¹,

Lemarchand²,

Zerrad³

et al. 2019

Opt. Express

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“…PSO was introduced by Eberhart and Kennedy a few decades ago, which required short memory and was applied as an easy implementation process. PSO has been extensively applied as an optimization technique, like optical stuff based on multilayer thin films [57], electric daily peakload forecasting [58], high-dimensional clustering statistics [59], prediction differential models [60], parameter approximation of chaotic plots [61], optimization of nonlinear benchmark model [62] and parameter estimate models in electromagnetic waves of the plane [63].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

Weera¹,

Sabir²,

Raja³

et al. 2022

Computers, Materials &Amp; Continua

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The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks (ANNs) with the use of global search particle swarm optimization (PSO) along with the competent local search interior-point programming (IPP) called as ANN-PSOIPP. The proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model (TON-DD-EFM). The TON-DD-EFM is based on two types along with the particulars of shape factor, delayed terms, and singular points. A merit function is performed using the optimization of PSOIPP to find the solutions to the TON-DD-EFM. The effectiveness of the ANN-PSOIPP is certified through the comparison with the exact results for solving four examples of the TON-DD-EFM. The scheme's efficiency is observed by performing the absolute error in suitable measures found around 10 −04 to 10 −07 . Furthermore, the statistical-based assessments for 100 trials are provided to compute the accuracy, stability, and constancy of the ANN-PSOIPP for solving the TON-DD-EFM.

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“…The Eberhart and Kennedy discovered PSO, which provide the short memory process and proceeds easily. Recently, it is broadly implemented in various applications, like electric daily peak-load prediction [35], multilayer thin film [36], statistics of high-dimensional clustering [37], prediction differential systems [38], approximation of parameters through chaotic plots [39], electromagnetic waves [40] and nonlinear benchmark systems [41] In search space investigations, a specific outcome in the optimization process is called a particle. The system is discovered by casually generated through PSO to form a swarm.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Numerical treatment for the nonlinear fifth kind of multi-singular differential model: a neuro-swarming approach

Sabir¹,

Ali²,

Alhazmi³

et al. 2022

Phys. Scr.

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In this study, a numerical scheme is proposed for the fifth order (FO) singular differential model (SDM), FO-SDM. The solutions of the singular form of the differential models are always considered difficult to solve and huge important in astrophysics. A neural network study together with the hybrid combination of global particle swarm optimization and local sequential quadratic programming schemes is provided to find the numerical simulations of the FO-SDM. An objective function is constructed using the differential FO-SDM along with the boundary conditions. The correctness of the scheme is observed by providing the comparison of the obtained and exact solutions. The justification of the proposed scheme is authenticated in terms of absolute error (AE), which is calculated in good measures for solving the FO-SDM. The efficiency and reliability of the stochastic approach are observed using the statistical performances to solve the FO-SDM.

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A computational approach for engineering optical properties of multilayer thin films: Particle swarm optimization applied to Bruggeman homogenization formalism

Cited by 11 publications

References 28 publications

Sensitivity of resonance properties of all-dielectric multilayers driven by statistical fluctuations

Sensitivity of resonance properties of all-dielectric multilayers driven by statistical fluctuations

Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

Numerical treatment for the nonlinear fifth kind of multi-singular differential model: a neuro-swarming approach

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