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International Journal for Uncertainty Quantification

Publicado 6 números por año

ISSN Imprimir: 2152-5080

ISSN En Línea: 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

MODEL CALIBRATION FOR DETONATION PRODUCTS: A PHYSICS-INFORMED, TIME-DEPENDENT SURROGATE METHOD BASED ON MACHINE LEARNING

Volumen 10, Edición 3, 2020, pp. 277-296
DOI: 10.1615/Int.J.UncertaintyQuantification.2020032977
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SINOPSIS

This paper proposes an innovative physics-informed and time-dependent surrogate method based on machine learning to calibrate the parameters of detonation products for cylinder test. Model calibration is a step of model validation, verification, and uncertainty quantification. A good calibration result will effectively enhance the credibility of a simulation, even model and software. This method extracts and quantifies the features of data, and corresponds them to the specific physical processes, such as the fluctuation caused by shock wave and the damping effect caused by energy dissipation. Different from the conventional surrogate models, our method gives a special consideration to the time variable and couples it with the detonation parameters properly through feature extraction and correlation analysis. The use of feature screening and variable selection enables this method to deal with high-dimensional and nonlinear situations. Models based on the Cramer-von Mises conditional statistic can reduce the complexity and improve the generalization performance by screening out the variables with strong correlation. And with the Oracle property of adaptive lasso, the convergence property of the method is guaranteed. Numerical examples of PBX9501 show, that the calibration results effectively improve the accuracy of simulation. With the relation between parameters and feature coefficients, we offer an instructive parameter adjusting strategy. Last but not least it can be generalized to other explosives. Model comparison results on 17 types of explosives show that our method has a better agreement with the cylinder test than the classical exponential form.

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