图书馆订阅: Guest
Begell Digital Portal Begell 数字图书馆 电子图书 期刊 参考文献及会议录 研究收集
国际不确定性的量化期刊
影响因子: 3.259 5年影响因子: 2.547 SJR: 0.417 SNIP: 0.8 CiteScore™: 1.52

ISSN 打印: 2152-5080
ISSN 在线: 2152-5099

Open Access

国际不确定性的量化期刊

DOI: 10.1615/Int.J.UncertaintyQuantification.2018021975
pages 175-192

FAST AND FLEXIBLE UNCERTAINTY QUANTIFICATION THROUGH A DATA-DRIVEN SURROGATE MODEL

Felix Dietrich
Technical University of Munich, Garching, 85747, Germany
Florian Künzner
Technical University of Munich, Garching, 85747, Germany
Tobias Neckel
Technical University of Munich, Garching, 85747, Germany
Gerta Köster
Munich University of Applied Sciences, Munich, 80335, Germany
Hans-Joachim Bungartz
Technical University of Munich, Garching, 85747, Germany

ABSTRACT

To assess a computer model's descriptive and predictive power, the model's response to uncertainties in the input must be quantified. However, simulations of complex systems typically need a lot of computational resources, and thus prohibit exhaustive sweeps of high-dimensional spaces. Moreover, the time available to compute a result for decision systems is often very limited. In this paper, we construct a data-driven surrogate model from time delays of observations of a complex, microscopic model. We employ diffusion maps to reduce the dimensionality of the delay space. The surrogate model allows faster generation of the quantity of interest over time than the original, microscopic model. It is a nonintrusive method, and hence does not need access to the model formulation. In contrast to most other surrogate approaches, the construction allows quantities of interest that are not closed dynamically, because a closed state space is constructed through Takens delay embedding. Also, the surrogate can be stored to and loaded from storage with very little effort. The surrogate model is decoupled from the original model, and the fast execution speed allows us to quickly evaluate many different parameter distributions. We demonstrate the capability of the approach in combination with forward UQ on a parametrized Burgers' equation, and the microscopic simulation of a train station. The surrogate model can accurately capture the dynamical features in both examples, with relative errors always smaller than 10%. The simulation time in the real-world example can be reduced by an order of magnitude.


Articles with similar content:

Vibration Control Analysis on Energy Saving for Data Centers using Optical Wireless Communication (OWC)
Second Thermal and Fluids Engineering Conference, Vol.1, 2017, issue
Jon P. Longtin, Kai Zheng, Himanshu Gupta
LOW-COST MULTI-DIMENSIONAL GAUSSIAN PROCESS WITH APPLICATION TO UNCERTAINTY QUANTIFICATION
International Journal for Uncertainty Quantification, Vol.5, 2015, issue 4
Guang Lin, Bledar A. Konomi
STATISTICAL SURROGATE MODELS FOR PREDICTION OF HIGH-CONSEQUENCE CLIMATE CHANGE
International Journal for Uncertainty Quantification, Vol.3, 2013, issue 4
Richard V. Field Jr., Paul Constantine, M. Boslough
DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING
International Journal for Uncertainty Quantification, Vol.4, 2014, issue 1
Bert J. Debusschere, Habib N. Najm, Peter Thornton, Cosmin Safta, Khachik Sargsyan, Daniel Ricciuto
PLUG AND PLAY FRAMEWORK FOR COMBINATORAL PROBLEM HEURISTICS
Flexible Automation and Intelligent Manufacturing, 1997:
Proceedings of the Seventh International FAIM Conference, Vol.0, 1997, issue
Andrew Wooster