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

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.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mona Fuhrländer ◽  
Sebastian Schöps
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Benchmark Problems ◽  
Yield Estimation ◽  
Electromagnetic Devices ◽  
Main Challenge ◽  
Sample Points ◽  
Stochastic Yield

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.

scholarly journals Accelerated Large-Scale Seismic Damage Simulation With a Bimodal Sampling Approach

2021 ◽  
Vol 7 ◽  
Author(s):  
Mohamadreza Sheibani ◽  
Ge Ou
Keyword(s):  
Surrogate Model ◽  
Large Scale ◽  
Sample Selection ◽  
Time History ◽  
Gaussian Process Regression ◽  
Skewed Distribution ◽  
Nonlinear Time History Analysis ◽  
Lumped Mass ◽  
Building Inventory ◽  
Damage Simulation

Regional damage simulation is a promising method to prepare organizations for the unforeseeable impact of a probable seismic natural hazard. Nonlinear time history analysis (NLTHA) of the finite element models (FEM) of the buildings in a region can provide resembling results to the actual buildings’ damages and responses. This approach requires large-scale computational resources, and to improve efficiency, parallel processing and representing building FEM models with lumped mass models are proposed. However, the computing complexity is still far-reaching when high-performance computing is not available. The building inventory of a region consists of numerous similar buildings with a limited number of distinct structures. In this paper, we propose a data-driven method that runs the NLTHA for the distinct structures exclusively and infers the damage and responses of other buildings using a surrogate model. Considering the skewed distribution of the buildings in a region, a novel informative sample selection method is proposed that is designed for bimodal sampling of the input domain. We use the Gaussian process regression as the surrogate model and compare the performance of different sample selection methods. The proposed method is able to approximate the results of the regional damage simulation regarding total economic loss estimation with 98.99% accuracy while reducing the computational demand to about 1/7th of the simulation processing time.

scholarly journals Short-Term Soil Moisture Forecasting via Gaussian Process Regression with Sample Selection

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3085
Author(s):  
Mingshuai Liu ◽  
Chao Huang ◽  
Long Wang ◽  
Yu Zhang ◽  
Xiong Luo
Keyword(s):  
Soil Moisture ◽  
Gaussian Process ◽  
Crop Production ◽  
Computing Time ◽  
Sample Selection ◽  
Gaussian Process Regression ◽  
Limiting Factor ◽  
Training Procedure ◽  
Probabilistic Forecasting ◽  
Design Algorithm

Soil moisture is a critical limiting factor for crop growth. Accurate soil moisture prediction helps to schedule irrigation and improve the crop production. A soil moisture prediction method based on Gaussian Process Regression (GPR) is proposed in this paper. In order to reduce the computation time of the GPR model, the Radially Uniform (RU) design algorithm was incorporated into the sample selection during the training procedure. Thus, representative training samples are identified and less training time is required. To validate the proposed prediction model, the soil moisture data collected in Beijing, China, was fully utilized. The experimental results demonstrate that the forecasting performance of the GPR model with the RU design algorithm is generally better than that of the generic GPR model in terms of less forecasting errors for both deterministic and probabilistic forecasting, while less computing time is needed for the model training.

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