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Numerical modeling of a linear photonic system for accurate and efficient time-domain simulations

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Photonics Research 2018年第6期6卷 560-573页

作者： YINGHAO YE DOMENICO SPINA YUFEI XING WIM BOGAERTS tom dhaene IDLab Department of Information TechnologyGhent University-imec Photonics Research Group Department of Information TechnologyGhent University-imec

In this paper, a novel modeling and simulation method for general linear, time-invariant, passive photonic devices and circuits is proposed. This technique, starting from the scattering parameters of the photonic system under study, builds a baseband equivalent state-space model that splits the optical carrier frequency and operates at baseband, thereby significantly reducing the modeling and simulation complexity without losing ***, it is possible to analytically reconstruct the port signals of the photonic system under study starting from the time-domain simulation of the corresponding baseband equivalent model. However, such equivalent models are complex-valued systems and, in this scenario, the conventional passivity constraints are not applicable anymore. Hence, the passivity constraints for scattering parameters and state-space models of baseband equivalent systems are presented, which are essential for time-domain simulations. Three suitable examples demonstrate the feasibility, accuracy, and efficiency of the proposed method.

关键词： Numerical modeling of a linear photonic system for accurate and efficient time-domain simulations

Stochastic collocation for device-level variability analysis in integrated photonics

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Photonics Research 2016年第2期4卷 93-100页

作者： Yufei Xing Domenico Spina Ang Li tom dhaene Wim Bogaerts Photonics Research Group Department of Information Technology Center for Nano and BiophotonicsGhent University imec Department of Information Technology Internet Based Communication Networks and Services (IBCN)Ghent University iMinds Luceda Photonics

We demonstrate the use of stochastic collocation to assess the performance of photonic devices under the effect of uncertainty. This approach combines high accuracy and efficiency in analyzing device variability with the ease of implementation of sampling-based methods. Its flexibility makes it suitable to be applied to a large range of photonic devices. We compare the stochastic collocation method with a Monte Carlo technique on a numerical analysis of the variability in silicon directional couplers.

关键词： Stochastic collocation for device-level variability analysis in integrated photonics

Compact representations of microstructure images using triplet networks

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npj Computational Materials 2020年第1期6卷 369-379页

作者： Michiel Larmuseau Michael Sluydts Koenraad Theuwissen Lode Duprez tom dhaene Stefaan Cottenier Center for Molecular Modeling Ghent UniversityTechnologiepark 46B-9052 ZwijnaardeBelgium Department of Electrical Energy MetalsMechanical Constructions and SystemsGhent UniversityTechnologiepark 46B-9052 ZwijnaardeBelgium IDLab Department of Information TechnologyGhent University-IMECTechnologiepark 126B-9052 ZwijnaardeBelgium OCAS NV/ArcelorMittal Global R&D Gent Pres.J.F.Kennedylaan 3B-9060 ZelzateBelgium

The microstructure of a material,typically characterized through a set of microscopy images of two-dimensional cross-sections,is a valuable source of information about the material and its *** pixel of the image is a degree of freedom causing the dimensionality of the information space to be extremely *** makes it difficult to recognize and extract all relevant information from the *** experts circumvent this by manually creating a lower-dimensional representation of the ***,the question of how a microstructure image can be best represented remains *** the field of deep learning,we present triplet networks as a method to build highly compact representations of the microstructure,condensing the relevant information into a much smaller number of *** demonstrate that these representations can be created even with a limited amount of example images,and that they are able to distinguish between visually very similar *** discuss the interpretability and generalization of the *** compact microstructure representations,it becomes easier to establish processing–structure–property links that are key to rational materials design.

关键词： networks Compact microstructure

基于遗传算法的多目标最小二乘支持向量机在近红外多组分定量分析中的应用

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光谱学与光谱分析 2014年第3期34卷 638-642页

作者：徐冰王星 dhaene tom 史新元 Couckuyt Ivo 白雁乔延江北京中医药大学中药信息工程研究中心北京100029 Ghent University-iMINDS Department of Information TechnologyGent B-9050Belgium 河南中医学院河南郑州450008

近红外(NIR)定量分析通常涉及多个组分,采用遗传算法和自适应建模策略,建立了能够对多组分同时定量的多目标最小二乘支持向量机(LS-SVM),并将其应用于玉米中四个组分和连翘中两个活性成分的NIR分析。结果表明多目标遗传算法配合自适应... 详细信息

近红外(NIR)定量分析通常涉及多个组分,采用遗传算法和自适应建模策略,建立了能够对多组分同时定量的多目标最小二乘支持向量机(LS-SVM),并将其应用于玉米中四个组分和连翘中两个活性成分的NIR分析。结果表明多目标遗传算法配合自适应建模策略可保证优化收敛于全局最优解。所建玉米多目标LS-SVM模型明显优于PLS1和PLS2模型;连翘多目标LS-SVM模型与PLS模型均可取得较好的校正和预测效果。两组数据中,径向基神经网络(RBFNN)模型均出现过拟合现象。多目标LS-SVM和单目标LS-SVM性能相近,但多目标LS-SVM建模运行一次即可得到结果,在NIR多组分定量分析中具有潜在应用优势。

关键词：多目标最小二乘支持向量机遗传算法近红外多组分定量自适应建模