Доступ предоставлен для: Guest
International Journal for Uncertainty Quantification
Главный редактор: Habib N. Najm (open in a new tab)
Ассоциированный редакторs: Dongbin Xiu (open in a new tab) Tao Zhou (open in a new tab)
Редактор-основатель: Nicholas Zabaras (open in a new tab)

Выходит 6 номеров в год

ISSN Печать: 2152-5080

ISSN Онлайн: 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

BEYOND BLACK-BOXES IN BAYESIAN INVERSE PROBLEMS AND MODEL VALIDATION: APPLICATIONS IN SOLID MECHANICS OF ELASTOGRAPHY

Том 8, Выпуск 5, 2018, pp. 447-482
DOI: 10.1615/Int.J.UncertaintyQuantification.2018025837
Get accessGet access

Краткое описание

The present paper is motivated by one of the most fundamental challenges in inverse problems, that of quantifying model discrepancies and errors. While significant strides have been made in calibrating model parameters, the overwhelming majority of pertinent methods is based on the assumption of a perfect model. Motivated by problems in solid mechanics which, as all problems in continuum thermodynamics, are described by conservation laws and phenomenological constitutive closures, we argue that in order to quantify model uncertainty in a physically meaningful manner, one should break open the black-box forward model. In particular, we propose formulating an undirected probabilistic model that explicitly accounts for the governing equations and their validity. This recasts the solution of both forward and inverse problems as probabilistic inference tasks where the problem's state variables should not only be compatible with the data but also with the governing equations as well. Even though the probability densities involved do not contain any black-box terms, they live in much higher-dimensional spaces. In combination with the intractability of the normalization constant of the undirected model employed, this poses significant challenges which we propose to address with a linearly scaling, double layer of stochastic variational inference. We demonstrate the capabilities and efficacy of the proposed model in synthetic forward and inverse problems (with and without model error) in elastography.

ЦИТИРОВАНО В
  1. Sarfaraz Muhammad S., Rosić Bojana V., Matthies Hermann G., Ibrahimbegović Adnan, Bayesian stochastic multi-scale analysis via energy considerations, Advanced Modeling and Simulation in Engineering Sciences, 7, 1, 2020. Crossref

  2. Noii Nima, Khodadadian Amirreza, Ulloa Jacinto, Aldakheel Fadi, Wick Thomas, François Stijn, Wriggers Peter, Bayesian Inversion with Open-Source Codes for Various One-Dimensional Model Problems in Computational Mechanics, Archives of Computational Methods in Engineering, 2022. Crossref

  3. Bayerlein Bernd, Hanke Thomas, Muth Thilo, Riedel Jens, Schilling Markus, Schweizer Christoph, Skrotzki Birgit, Todor Alexandru, Moreno Torres Benjami, Unger Jörg F., Völker Christoph, Olbricht Jürgen, A Perspective on Digital Knowledge Representation in Materials Science and Engineering, Advanced Engineering Materials, 24, 6, 2022. Crossref

  4. Mohammadi Narges, Doyley Marvin M., Cetin Mujdat, A Statistical Framework for Model-Based Inverse Problems in Ultrasound Elastography, 2020 54th Asilomar Conference on Signals, Systems, and Computers, 2020. Crossref

  5. Coelho Lima Isabela, Robens-Radermacher Annika, Titscher Thomas, Kadoke Daniel, Koutsourelakis Phaedon-Stelios, Unger Jörg F., Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forward model’s accuracy, Computational Mechanics, 2022. Crossref

  6. Xia Yingzhi, Zabaras Nicholas, Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems, Journal of Computational Physics, 455, 2022. Crossref

Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции Цены и условия подписки Begell House Контакты Language English 中文 Русский Português German French Spain