Accurate Reduced Dimensional Polynomial Chaos for Efficient Uncertainty Quantification of Microwave/RF Networks

Prasad, Aditi Krishna; Roy, Sourajeet

doi:10.1109/tmtt.2017.2689742

Cited by 34 publications

(14 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper presents an alternative technique for the UQ in high-dimensional problems [18]- [20], such as the partial least squares (PLS) regression [21]. The PLS regression allows to build a surrogate model of an output depending on many uncertain parameters.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Parameter Variability in an Integrated Wireless Power Transfer System via Partial Least-Squares Regression

Larbi

Trinchero

Canavero

et al. 2020

IEEE Trans. Compon., Packag. Manufact. Technol.

View full text Add to dashboard Cite

This paper deals with the application of the partial least squares (PLS) regression to the uncertainty quantification of an integrated wireless power transfer with 30 random variables. It considers the development of surrogate models using a limited set of training samples in order to estimate statistical quantities of the converter efficiency with a relatively low computational cost compared to the standard brute force Monte Carlo (MC) simulation. The strength, the performance and the features of the proposed modeling approach are then compared with the ones of an advanced probabilistic surrogate model combining the Leastsquares Support Vector Machine (LS-SVM) and the Gaussian Process (GP) regression. The MC simulation is considered as reference.

show abstract

Section: Introductionmentioning

confidence: 99%

Analysis of Parameter Variability in an Integrated Wireless Power Transfer System via Partial Least-Squares Regression

Larbi

Trinchero

Canavero

et al. 2020

IEEE Trans. Compon., Packag. Manufact. Technol.

View full text Add to dashboard Cite

show abstract

“…As a result, they are easier to implement, comparing to intrusive methods [9]. For problems with higher dimensions, non-intrusive hierarchical sparse PC approaches have been used, which the PC expansion terms are reduced based form of conventional full-blown PC expansions in general [10][11][12][13][14]. For eliminating the computational cost of model construction due to the large size of PC expansions, in [14], a new approach has been presented, which ends up in lower PC expansion with keeping the model accurate.…”

Section: Introductionmentioning

confidence: 99%

Residue-Pole Methods for Variability Analysis of S-parameters of Microwave Devices with 3D FEM and Mesh Deformation

Rufuie¹,

Lamęcki²,

Sypek³

et al. 2020

RADIOENGINEERING

View full text Add to dashboard Cite

This paper presents a new approach for variability analysis of microwave devices with a high dimension of uncertain parameters. The proposed technique is based on modeling an approximation of system by its poles and residues using several modeling methods, including ordinary kriging, Adaptive Polynomial Chaos (APCE), and Support Vector Machine Regression (SVM). The computational cost is compared with the traditional Monte-Carlo method. To improve the efficiency, mesh deformation is applied within 3D FEM framework.

show abstract

“…Although this technique has been satisfactorily employed in many examples, it suffers when the input dimensionality of the problem significantly increases, since the computational cost of the numerical model rapidly grows. To face this problem, referred to as the curse of dimensionality, few approaches have been lately introduced in the context of high-speed circuits [8,9]. In the field of probabilistic engineering mechanics, another approach named the sparse PC and based on the Least Angle Regression (LARS) algorithm [10] has been successfully used in order to quantify uncertainties in high-dimensional problems.…”

Section: Introductionmentioning

confidence: 99%

Identfication of Main Factors of Uncertainty in a Microstrip Line Network

Larbi¹,

Stievano²,

Canavero³

et al. 2018

PIER

View full text Add to dashboard Cite

This paper deals with uncertainty propagation applied to the analysis of crosstalk in printed circuit board microstrip traces. Complex interconnection networks generally are affected by many uncertain parameters and their point-to-point transfer functions are computationally expensive, thus making Monte-Carlo analyses rather inefficient. To overcome this situation, a metamodel is highly desirable. This paper presents a sparse and accelerated polynomial chaos approach, which proves to be well adapted for high-dimensional uncertainty quantification and well suited for the sensitivity analysis of crosstalk effects. We highlight the significant advantage of the advocated approach for the design of microstrip line networks of complex topology. In fact, we demonstrate how a small number of system simulations can help to quantify the statistics of the output variability and identify a reduced set of high-impact parameters.

show abstract

Accurate Reduced Dimensional Polynomial Chaos for Efficient Uncertainty Quantification of Microwave/RF Networks

Cited by 34 publications

References 47 publications

Analysis of Parameter Variability in an Integrated Wireless Power Transfer System via Partial Least-Squares Regression

Analysis of Parameter Variability in an Integrated Wireless Power Transfer System via Partial Least-Squares Regression

Residue-Pole Methods for Variability Analysis of S-parameters of Microwave Devices with 3D FEM and Mesh Deformation

Identfication of Main Factors of Uncertainty in a Microstrip Line Network

Contact Info

Product

Resources

About