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International Journal for Multiscale Computational Engineering

Published 6 issues per year

ISSN Print: 1543-1649

ISSN Online: 1940-4352

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: 1.4 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: 1.3 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: 2.2 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: 0.00034 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: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

Multiscale Computational Strategy With Time and Space Homogenization: A Radial-Type Approximation Technique for Solving Microproblems

Volume 2, Issue 4, 2004, 18 pages
DOI: 10.1615/IntJMultCompEng.v2.i4.40
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ABSTRACT

A new multiscale computational strategy for the analysis of structures (such as composite structures) described in detail both in space and in time was introduced recently. This strategy is iterative and involves an automatic homogenization procedure in space as well as in time. At each iteration, this procedure requires the resolution of a large number of linear evolution equations, called the microproblems, on the microscale. In this paper, we present a robust approximate resolution technique for these microproblems based on the concept of radial approximation. This very general technique, which leads to the construction of a relevant reduced basis of space functions, is particularly suitable for the analysis of composite structures.

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  2. Nouy Anthony, Recent Developments in Spectral Stochastic Methods for the Numerical Solution of Stochastic Partial Differential Equations, Archives of Computational Methods in Engineering, 16, 3, 2009. Crossref

  3. Chinesta F., Ammar A., Cueto E., Proper generalized decomposition of multiscale models, International Journal for Numerical Methods in Engineering, 83, 8-9, 2010. Crossref

  4. Nouy Anthony, A priori model reduction through Proper Generalized Decomposition for solving time-dependent partial differential equations, Computer Methods in Applied Mechanics and Engineering, 199, 23-24, 2010. Crossref

  5. Ladevèze Pierre, Chamoin Ludovic, On the verification of model reduction methods based on the proper generalized decomposition, Computer Methods in Applied Mechanics and Engineering, 200, 23-24, 2011. Crossref

  6. Chinesta Francisco, Ladeveze Pierre, Cueto Elías, A Short Review on Model Order Reduction Based on Proper Generalized Decomposition, Archives of Computational Methods in Engineering, 18, 4, 2011. Crossref

  7. Nouy Anthony, A generalized spectral decomposition technique to solve a class of linear stochastic partial differential equations, Computer Methods in Applied Mechanics and Engineering, 196, 45-48, 2007. Crossref

  8. Nouy Anthony, Generalized spectral decomposition method for solving stochastic finite element equations: Invariant subspace problem and dedicated algorithms, Computer Methods in Applied Mechanics and Engineering, 197, 51-52, 2008. Crossref

  9. Falcó A., Nouy A., A Proper Generalized Decomposition for the solution of elliptic problems in abstract form by using a functional Eckart–Young approach, Journal of Mathematical Analysis and Applications, 376, 2, 2011. Crossref

  10. Ladevèze Pierre, Néron David, Gosselet Pierre, On a mixed and multiscale domain decomposition method, Computer Methods in Applied Mechanics and Engineering, 196, 8, 2007. Crossref

  11. Nouy Anthony, Méthode de construction de bases spectrales généralisées pour l'approximation de problèmes stochastiques, Mécanique & Industries, 8, 3, 2007. Crossref

  12. Ammar Amine, Chinesta Francisco, Cueto Elías, Doblaré Manuel, Proper generalized decomposition of time-multiscale models, International Journal for Numerical Methods in Engineering, 90, 5, 2012. Crossref

  13. Nouy Anthony, Proper Generalized Decompositions and Separated Representations for the Numerical Solution of High Dimensional Stochastic Problems, Archives of Computational Methods in Engineering, 17, 4, 2010. Crossref

  14. Néron David, Ladevèze Pierre, Proper Generalized Decomposition for Multiscale and Multiphysics Problems, Archives of Computational Methods in Engineering, 17, 4, 2010. Crossref

  15. Lamari H., Ammar A., Cartraud P., Legrain G., Chinesta F., Jacquemin F., Routes for Efficient Computational Homogenization of Nonlinear Materials Using the Proper Generalized Decompositions, Archives of Computational Methods in Engineering, 17, 4, 2010. Crossref

  16. Boucinha L., Gravouil A., Ammar A., Space–time proper generalized decompositions for the resolution of transient elastodynamic models, Computer Methods in Applied Mechanics and Engineering, 255, 2013. Crossref

  17. Bouclier Robin, Louf François, Chamoin Ludovic, Real-time validation of mechanical models coupling PGD and constitutive relation error, Computational Mechanics, 52, 4, 2013. Crossref

  18. Cremonesi M., Néron D., Guidault P.-A., Ladevèze P., A PGD-based homogenization technique for the resolution of nonlinear multiscale problems, Computer Methods in Applied Mechanics and Engineering, 267, 2013. Crossref

  19. Louf F., Champaney L., Fast validation of stochastic structural models using a PGD reduction scheme, Finite Elements in Analysis and Design, 70-71, 2013. Crossref

  20. Cherabi Bilal, Hamrani Abderrachid, Belaidi Idir, Khelladi Sofiane, Bakir Farid, An efficient reduced-order method with PGD for solving journal bearing hydrodynamic lubrication problems, Comptes Rendus Mécanique, 344, 10, 2016. Crossref

  21. Ladevèze Pierre, On reduced models in nonlinear solid mechanics, European Journal of Mechanics - A/Solids, 60, 2016. Crossref

  22. Chinesta Francisco, Huerta Antonio, Rozza Gianluigi, Willcox Karen, Model Reduction Methods, in Encyclopedia of Computational Mechanics Second Edition, 2017. Crossref

  23. Chinesta Francisco, Cueto Elías, Introduction, in PGD-Based Modeling of Materials, Structures and Processes, 2014. Crossref

  24. Chinesta Francisco, Ammar Amine, Cueto Elías, Recent Advances and New Challenges in the Use of the Proper Generalized Decomposition for Solving Multidimensional Models, Archives of Computational Methods in Engineering, 17, 4, 2010. Crossref

  25. Chinesta Francisco, Keunings Roland, Leygue Adrien, Introduction, in The Proper Generalized Decomposition for Advanced Numerical Simulations, 2014. Crossref

  26. Cheung Siu Wun, Guha Nilabja, Dynamic data-driven Bayesian GMsFEM, Journal of Computational and Applied Mathematics, 353, 2019. Crossref

  27. Scanff R., Nachar S., Boucard P. -A., Néron D., A Study on the LATIN-PGD Method: Analysis of Some Variants in the Light of the Latest Developments, Archives of Computational Methods in Engineering, 28, 5, 2021. Crossref

  28. Han Shuai, Shi Chunxiang, Sun Shuai, Gu Junxia, Xu Bin, Liao Zhihong, Zhang Yu, Xu Yanqin, Development and Evaluation of a Real-Time Hourly One-Kilometre Gridded Multisource Fusion Air Temperature Dataset in China Based on Remote Sensing DEM, Remote Sensing, 14, 10, 2022. Crossref

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