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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 STRUCTURAL INFERENCE USING LOCAL DYNAMIC OPERATORS

Volumen 9, Edición 1, 2019, pp. 59-83
DOI: 10.1615/Int.J.UncertaintyQuantification.2019025828
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SINOPSIS

This paper focuses on the problem of quantifying the effects of model-structure uncertainty in the context of time-evolving dynamical systems. This is motivated by multi-model uncertainty in computer physics simulations: developers often make different modeling choices in numerical approximations and process simplifications, leading to different numerical codes that ostensibly represent the same underlying dynamics. We consider model-structure inference as a two-step methodology: the first step is to perform system identification on numerical codes for which it is possible to observe the full state; the second step is structural uncertainty quantification, in which the goal is to search candidate models "close" to the numerical code surrogates for those that best match a quantity of interest (QOI) from some empirical data sets. Specifically, we (1) define a discrete, local representation of the structure of a partial differential equation, which we refer to as the "local dynamical operator" (LDO); (2) identify model structure nonintrusively from numerical code output; (3) nonintrusively construct a reduced-order model (ROM) of the numerical model through POD-DEIM-Galerkin projection; (4) perturb the ROM dynamics to approximate the behavior of alternate model structures; and (5) apply Bayesian inference and energy conservation laws to calibrate a LDO to a given QOI. We demonstrate these techniques using the two-dimensional rotating shallow water equations as an example system.

CITADO POR
  1. Nadiga Balasubramanya, Jiang Chiyu, Livescu Daniel, Leveraging Bayesian analysis to improve accuracy of approximate models, Journal of Computational Physics, 394, 2019. Crossref

  2. Bochev Pavel, Paskaleva Biliana, Development of data‐driven exponential integrators with application to modeling of delay photocurrents, Numerical Methods for Partial Differential Equations, 2021. Crossref

  3. Nadiga Balasubramanya T., Reservoir Computing as a Tool for Climate Predictability Studies, Journal of Advances in Modeling Earth Systems, 13, 4, 2021. Crossref

  4. Chen Xiaoqian, Nadiga Balasubramanya T., Timofeyev Ilya, Predicting shallow water dynamics using echo-state networks with transfer learning, GEM - International Journal on Geomathematics, 13, 1, 2022. Crossref

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