Library Subscription: Guest
International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 2152-5080

ISSN Online: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

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

Volume 8, Issue 2, 2018, pp. 175-192
DOI: 10.1615/Int.J.UncertaintyQuantification.2018021975
Get accessGet access

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.

CITED BY
  1. Künzner Florian, Neckel Tobias, Bungartz Hans-Joachim, Prediction and reduction of runtime in non-intrusive forward UQ simulations, SN Applied Sciences, 1, 9, 2019. Crossref

  2. Lehmberg Daniel, Dietrich Felix, Kevrekidis Ioannis G., Bungartz Hans-Joachim, Köster Gerta, Exploring Koopman Operator Based Surrogate Models—Accelerating the Analysis of Critical Pedestrian Densities, in Traffic and Granular Flow 2019, 252, 2020. Crossref

  3. Rahn Simon, Gödel Marion, Fischer Rainer, Köster Gerta, Dynamics of a Simulated Demonstration March: An Efficient Sensitivity Analysis, Sustainability, 13, 6, 2021. Crossref

  4. Mayr Christina Maria, Schuhbäck Stefan, Wischhof Lars, Köster Gerta, Analysis of information dissemination through direct communication in a moving crowd, Safety Science, 142, 2021. Crossref

  5. Mufti Bilal, Chen Mengzhen, Perron Christian, Mavris Dimitri N., A Multi-Fidelity Approximation of the Active Subspace Method for Surrogate Models with High-Dimensional Inputs, AIAA AVIATION 2022 Forum, 2022. Crossref

Begell Digital Portal Begell Digital Library eBooks Journals References & Proceedings Research Collections Prices and Subscription Policies Begell House Contact Us Language English 中文 Русский Português German French Spain