Inscrição na biblioteca: Guest
International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 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

AN OVERVIEW OF INVERSE MATERIAL IDENTIFICATION WITHIN THE FRAMEWORKS OF DETERMINISTIC AND STOCHASTIC PARAMETER ESTIMATION

Volume 3, Edição 4, 2013, pp. 289-319
DOI: 10.1615/Int.J.UncertaintyQuantification.2012003668
Get accessDownload

RESUMO

This work investigates the problem of parameter estimation within the frameworks of deterministic and stochastic parameter estimation methods. For the deterministic methods, we look at constrained and unconstrained optimization approaches. For the constrained optimization approaches we study three different formulations: L2, error in constitutive equation method (ECE), and the modified error in constitutive equation (MECE) method. We investigate these formulations in the context of both Tikhonov and total variation (TV) regularization. The constrained optimization approaches are compared with an unconstrained nonlinear least-squares (NLLS) approach. In the least-squares framework we investigate three different formulations: standard, MECE, and ECE. With the stochastic methods, we first investigate Bayesian calibration, where we use Monte Carlo Markov chain (MCMC) methods to calculate the posterior parameter estimates. For the Bayesian methods, we investigate the use of a standard likelihood function, a likelihood function that incorporates MECE, and a likelihood function that incorporates ECE. Furthermore, we investigate the maximum a posteriori (MAP) approach. In the MAP approach, parameters′ full posterior distribution are not generated via sampling; however, parameter point estimates are computed by searching for the values that maximize the parameters′ posterior distribution. Finally, to achieve dimension reduction in both the MCMC and NLLS approaches, we approximate the parameter field with radial basis functions (RBF). This transforms the parameter estimation problem into one of determining the governing parameters for the RBF.

CITADO POR
  1. Koner Prabhat K., Harris Andrew, Maturi Eileen, A Physical Deterministic Inverse Method for Operational Satellite Remote Sensing: An Application for Sea SurfaceTemperature Retrievals, IEEE Transactions on Geoscience and Remote Sensing, 53, 11, 2015. Crossref

  2. Koner Prabhat K., Harris Andrew R., Dash Prasanjit, A Deterministic Method for Profile Retrievals From Hyperspectral Satellite Measurements, IEEE Transactions on Geoscience and Remote Sensing, 54, 10, 2016. Crossref

  3. Kirchdoerfer T., Ortiz M., Data Driven Computing with noisy material data sets, Computer Methods in Applied Mechanics and Engineering, 326, 2017. Crossref

  4. Kirchdoerfer T., Ortiz M., Data-driven computing in dynamics, International Journal for Numerical Methods in Engineering, 113, 11, 2018. Crossref

  5. Kirchdoerfer Trenton, Ortiz Michael, Data-Driven Computing, in Advances in Computational Plasticity, 46, 2018. Crossref

  6. Koner Prabhat, Dash Prasanjit, Maximizing the Information Content of Ill-Posed Space-Based Measurements Using Deterministic Inverse Method, Remote Sensing, 10, 7, 2018. Crossref

  7. Koner Prabhat K., A transformative approach to enhance the parameter information from microwave and infrared remote sensing measurements, Big Earth Data, 4, 3, 2020. Crossref

Portal Digital Begell Biblioteca digital da Begell eBooks Diários Referências e Anais Coleções de pesquisa Políticas de preços e assinaturas Begell House Contato Language English 中文 Русский Português German French Spain