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Activity Number: 582 - Nonparametric Methods for Statistical Inference
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #305192 Presentation
Title: Estimation of an Improved Surrogate Model in Uncertainty Quantification by Neural Networks
Author(s): Sebastian Kersting* and Michael Kohler and Benedict Götz
Companies: TU Darmstadt and Technische Universitaet Darmstadt and TU Darmstadt
Keywords: curse of dimensionality; density estimation; imperfect models; neural networks; surrogate models; uncertainty quantification

Quantification of uncertainty of a technical system is often based on a surrogate model of a corresponding simulation model. In any application the simulation model will not describe the reality perfectly, and consequently the surrogate model will be imperfect. We propose a method to combine observed data from the technical system with simulated data from the imperfect simulation model in order to estimate an improved surrogate model consisting of multi-layer feedforward neural networks, and show that under suitable assumptions this estimate is able to circumvent the curse of dimensionality. Based on this improved surrogate model we show a rate of convergence result for density estimates. The practical usefulness of the newly proposed estimates is demonstrated by using them to predict the uncertainty of a lateral vibration attenuation system with piezo-elastic supports.

Authors who are presenting talks have a * after their name.

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