A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices

Abstract In this paper an efficient and reliable method for stochastic yield estimation is presented. Since one main challenge of uncertainty quantification is the computational feasibility, we propose a hybrid approach where most of the Monte Carlo sample points are evaluated with a surrogate model, and only a few sample points are reevaluated with the original high fidelity model. Gaussian process regression is a non-intrusive method which is used to build the surrogate model. Without many prerequisites, this gives us not only an approximation of the function value, but also an error indicator that we can use to decide whether a sample point should be reevaluated or not. For two benchmark problems, a dielectrical waveguide and a lowpass filter, the proposed methods outperform classic approaches.

Download Full-text

Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach

Advances in Radio Science ◽

10.5194/ars-19-41-2021 ◽

2021 ◽

Vol 19 ◽

pp. 41-48

Author(s):

Mona Fuhrländer ◽

Sebastian Schöps

Keyword(s):

Gaussian Process ◽

Hybrid Approach ◽

Gaussian Process Regression ◽

Monte Carlo Analysis ◽

Computational Effort ◽

Benchmark Problem ◽

Yield Optimization ◽

Multi Objective ◽

Electromagnetic Devices ◽

The Impact

Abstract. Quantification and minimization of uncertainty is an important task in the design of electromagnetic devices, which comes with high computational effort. We propose a hybrid approach combining the reliability and accuracy of a Monte Carlo analysis with the efficiency of a surrogate model based on Gaussian Process Regression. We present two optimization approaches. An adaptive Newton-MC to reduce the impact of uncertainty and a genetic multi-objective approach to optimize performance and robustness at the same time. For a dielectrical waveguide, used as a benchmark problem, the proposed methods outperform classic approaches.

Download Full-text

Surrogate model for an aligned-spin effective-one-body waveform model of binary neutron star inspirals using Gaussian process regression

Physical Review D ◽

10.1103/physrevd.100.024002 ◽

2019 ◽

Vol 100 (2) ◽

Cited By ~ 18

Author(s):

Benjamin D. Lackey ◽

Michael Pürrer ◽

Andrea Taracchini ◽

Sylvain Marsat

Keyword(s):

Neutron Star ◽

Gaussian Process ◽

Surrogate Model ◽

Gaussian Process Regression ◽

Binary Neutron Star

Download Full-text

Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction

Computational Intelligence and Neuroscience ◽

10.1155/2019/2859429 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 14

Author(s):

George Kopsiaftis ◽

Eftychios Protopapadakis ◽

Athanasios Voulodimos ◽

Nikolaos Doulamis ◽

Aristotelis Mantoglou

Keyword(s):

Gaussian Process ◽

Seawater Intrusion ◽

Surrogate Model ◽

Gaussian Process Regression ◽

Variable Density ◽

Regression Method ◽

Bayesian Optimization ◽

Coefficient Of Determination ◽

Support Vector ◽

Pumping Rate

Accurate prediction of the seawater intrusion extent is necessary for many applications, such as groundwater management or protection of coastal aquifers from water quality deterioration. However, most applications require a large number of simulations usually at the expense of prediction accuracy. In this study, the Gaussian process regression method is investigated as a potential surrogate model for the computationally expensive variable density model. Gaussian process regression is a nonparametric kernel-based probabilistic model able to handle complex relations between input and output. In this study, the extent of seawater intrusion is represented by the location of the 0.5 kg/m3 iso-chlore at the bottom of the aquifer (seawater intrusion toe). The initial position of the toe, expressed as the distance of the specific line from a number of observation points across the coastline, along with the pumping rates are the surrogate model inputs, whereas the final position of the toe constitutes the output variable set. The training sample of the surrogate model consists of 4000 variable density simulations, which differ not only in the pumping rate pattern but also in the initial concentration distribution. The Latin hypercube sampling method is used to obtain the pumping rate patterns. For comparison purposes, a number of widely used regression methods are employed, specifically regression trees and Support Vector Machine regression (linear and nonlinear). A Bayesian optimization method is applied to all the regressors, to maximize their efficiency in the prediction of seawater intrusion. The final results indicate that the Gaussian process regression method, albeit more time consuming, proved to be more efficient in terms of the mean absolute error (MAE), the root mean square error (RMSE), and the coefficient of determination (R2).

Download Full-text

Use of an Active Learning Strategy Based on Gaussian Process Regression for the Uncertainty Quantification of Electronic Devices

Engineering Proceedings ◽

10.3390/iec2020-06967 ◽

2020 ◽

Vol 3 (1) ◽

pp. 3

Author(s):

Riccardo Trinchero ◽

Flavio Canavero

Keyword(s):

Active Learning ◽

Gaussian Process ◽

Uncertainty Quantification ◽

Surrogate Model ◽

Learning Strategy ◽

Sample Selection ◽

Gaussian Process Regression ◽

Latin Hypercube Sampling ◽

Buck Converter ◽

Equivalent Model

This paper presents a preliminary version of an active learning (AL) scheme for the sample selection aimed at the development of a surrogate model for the uncertainty quantification based on the Gaussian process regression. The proposed AL strategy iteratively searches for new candidate points to be included within the training set by trying to minimize the relative posterior standard deviation provided by the Gaussian process regression surrogate. The above scheme has been applied for the construction of a surrogate model for the statistical analysis of the efficiency of a switching buck converter as a function of seven uncertain parameters. The performance of the surrogate model constructed via the proposed active learning method is compared with that provided by an equivalent model built via a Latin hypercube sampling. The results of a Monte Carlo simulation with the computational model are used as reference.

Download Full-text