A Blackbox Yield Estimation Workflow with Gaussian Process Regression for Industrial Problems

03/30/2020
by   Mona Fuhrländer, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 6

page 8

12/20/2019

Adaptive Newton-Monte Carlo for efficient and fully error controlled yield optimization

In this paper we present an efficient and fully error controlled algorit...
07/12/2017

Large Scale Variable Fidelity Surrogate Modeling

Engineers widely use Gaussian process regression framework to construct ...
09/27/2017

Gaussian process modelling using UQLab

We introduce the Gaussian process modelling module of the UQLab software...
09/20/2019

Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation

The increasing penetration of renewable energy resources in power system...
10/08/2020

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

Quantification and minimization of uncertainty is an important task in t...
09/23/2021

Multi-Fidelity Surrogate Modeling for Time-Series Outputs

This paper considers the surrogate modeling of a complex numerical code ...
02/12/2020

Uncertainty Quantification of Mode Shape Variation Utilizing Multi-Level Multi-Response Gaussian Process

Mode shape information play the essential role in deciding the spatial p...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.