EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 24 (2023) Mechanics & Industry, 24 (2023) 32 References
Issue
Mechanics & Industry
Volume 24, 2023
History of matter: from its raw state to its end of life
Article Number 32
Number of page(s) 27
DOI https://doi.org/10.1051/meca/2023027
Published online 08 September 2023
  1. U.D.O. Defense, Technomic Publishing Company, Materials Sciences Corporation, American Society for Testing, and Materials, Composite Materials Handbook-MIL 17, Vol. III: Materials Usage, Design, and Analysis, The Composite Materials Handbook-MIL 17. Taylor & Francis, 1999 [Google Scholar]
  2. A. Tarantola, Inverse Problem Theory and Methods for Model Parameter Estimation, Society for Industrial and Applied Mathematics, 2005. [CrossRef] [Google Scholar]
  3. L. Wasserman, All of Statistics: A Concise Course in Statistical Inference, Springer Publishing Company, Incorporated, 2010 [Google Scholar]
  4. A. Antoniadis, R. Carmona, Regression non lineaire et applications. Economie et statistiques avan¸c´ees, Economica, 1992 [Google Scholar]
  5. G.C. Ballesteros, P. Angelikopoulos, C. Papadimitriou, P. Koumoutsakos, Bayesian hierarchical models for uncertainty quantification in structural dynamics, Vulnerabil. Uncertain. Risk: Quantif. Mitig. Manag. 162, 1615–1624 (2014) [CrossRef] [Google Scholar]
  6. M. Song, I. Behmanesh, B. Moaveni, C. Papadimitriou, Accounting for modeling errors and inherent structural variability through a hierarchical bayesian model updating approach: an overview, Sensors 20, 3874–3901 (2020) [CrossRef] [PubMed] [Google Scholar]
  7. E. Demidenko, Mixed Models. Theory and Applications with R, John Wiley & Sons, 2013 [Google Scholar]
  8. M. Lavielle, Mixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools, CRC press, 2014 [CrossRef] [Google Scholar]
  9. P. Congdon, Bayesian Statistical Modelling, John Wiley & Sons, 2007 [Google Scholar]
  10. C. Laboulfie, M. Balesdent, L. Brevault, F.-X. Irisarri, J.-F. Maire, S. Da Veiga, R. Le Riche, Calibration of material model parameters using mixed-effects booktitle = 25ème Congres Français de Mecanique, model, 2021, pp. 258–295 [Google Scholar]
  11. J. Lemaitre, J.-L. Chaboche, Mechanics of solid materials, Cambridge University Press, 1994 [Google Scholar]
  12. J. Lemaitre, R. Desmorat. Engineering Damage Mechanics: Ductile, Creep, Fatigue and Brittle Failures. Springer Science & Business Media, 2006 [Google Scholar]
  13. S. Moreau, A. Chrysochoos, J.-M. Muracciole, B. Wattrisse, Analysis of thermoelastic effects accompanying the deformation of pmma and pc polymers, Comptes Rendus Mec. 333, 648–653 (2005) [CrossRef] [Google Scholar]
  14. R. Schapery, On the characterization of nonlinear viscoelastic materials, Polymer Eng. Sci. 9, 295–310 (1969) [CrossRef] [Google Scholar]
  15. J.-L. Chaboche, A review of some plasticity and viscoplasticity constitutive Theories, Int. J. Plasticity 24, 1642–1693 (2008) [CrossRef] [Google Scholar]
  16. B. Miled, I. Doghri, L. Delannay, Coupled viscoelastic-viscoplastic modeling of homogeneous and isotropic polymers: numerical algorithm and analytical solutions. Comput. Methods Appl. Mech. Eng. 200, 3381–3394 (2011). [Google Scholar]
  17. A. Krairi, I. Doghri, A thermodynamically-based constitutive model for thermoplastic polymers coupling viscoelasticity, viscoplasticity and ductile damage, Int. J. Plasticity 60, 163–181 (2014) [Google Scholar]
  18. F. Praud, G. Chatzigeorgiou, J. Bikard, F. Meraghni, Phenomenological multi-mechanisms constitutive modelling for thermoplastic polymers, implicit implementation and experimental validation, Mech. Mater. 114, 9–29 (2017) [Google Scholar]
  19. C. Hochard, P.-A. Aubourg, J.-P. Charles, Modelling of the mechanical behaviour of woven-fabric cfrp laminates up to failure, Composites Sci. Technol. 61, 221–230 (2001) [CrossRef] [Google Scholar]
  20. P. Ladeveze, E. LeDantec, Damage modelling of the elementary ply for laminated composites, Composites Sci. Technol. 43, 257–267 (1992) [CrossRef] [Google Scholar]
  21. A. Launay, M.H. Maitournam, Y. Marco, I. Raoult, F. Szmytka, Cyclic behaviour of short glass fibre reinforced polyamide: experimental study and constitutive equations, Int. J. Plasticity 27, 1267–1293 (2011) [CrossRef] [Google Scholar]
  22. F. Praud, G. Chatzigeorgiou, Y. Chemisky, F. Meraghni, Hybrid micromechanical-phenomenological modelling of anisotropic damage and anelasticity induced by microcracks in unidirectional composites, Composite Struct. 182, 223–236 (2017) [CrossRef] [Google Scholar]
  23. F. Lachaud, Delaminage de materiaux composites a fibres de carbone et a matrices organiques: etude numerique et experimentale, suivi par emission acoustique. PhD thesis, 1997 [Google Scholar]
  24. A. Forrester and A. Sobester, A.and Keane. Engineering Design via Surrogate Modelling – A Practical Guide, Wiley, 2008 [CrossRef] [Google Scholar]
  25. J.-M. Berthelot, Materiaux Composites-Comportement Mecanique et Analyse des Structures, 2012. [Google Scholar]
  26. M. Gallagher, J. Doherty, Parameter estimation and uncertainty analysis for a watershed model, Environ. Model. Softw. 22, 1000–1020 (2007) [CrossRef] [Google Scholar]
  27. M. Kennedy, A. O’Hagan, Bayesian calibration of computer models, J. Roy. Stat. Soc. B (Stat. Methodol.) 63, 425–464 (2001) [CrossRef] [Google Scholar]
  28. K. Levenberg, A method for the solution of certain nonlinear problems in least Squares, Q. Appl. Math. 2, 164–168 (1944) [CrossRef] [Google Scholar]
  29. D.W. Marquardt, An algorithm for least-squares estimation of nonlinear parameter, J. Soc. Ind. Appl. Math. 11, 431–441 (1963) [CrossRef] [Google Scholar]
  30. R. Pintelon, J. Schoukens, System Identification, John Wiley & Sons, Ltd, 2012 [CrossRef] [Google Scholar]
  31. F. Meraghni, Y. Chemisky, B. Piotrowski, R. Echchorfi, N. Bourgeois, E. Patoor, Parameter identification of a thermodynamic model for superelastic shape memory alloys using analytical calculation of the sensitivity matrix, Eur. J. Mech. A/Solids 45, 226–237 (2014) [CrossRef] [Google Scholar]
  32. J. Pinheiro, D. Bates, Mixed-Effect Models in S and S-plus, Vol. 96, 2002 [Google Scholar]
  33. J.B. Nagel, B. Sudret, A unified framework for multilevel uncertainty quantification in Bayesian inverse problems, Probab. Eng. Mech. 43 68–84 (2016) [CrossRef] [Google Scholar]
  34. S.L. Beal, L.B. Sheiner, Estimating population kinetics, Crit. Rev. Biomed. Eng. 8, 195–222 (1982) [Google Scholar]
  35. D. Blei, A. Kucukelbir, J. McAuliff, Variational inference: a review for statisticians, J. Am. Stat. Assoc. 112, 859–877 (2017) [CrossRef] [Google Scholar]
  36. J. Wakefield, The Bayesian analysis of population pharmacokinetic models, J. Am. Stat. Assoc. 91, 62–75 (1996) [CrossRef] [Google Scholar]
  37. P. Damlen, J. Wakefield, S. Walker, Gibbs sampling for Bayesian non-conjugate and hierarchical models by using auxiliary variables, J. Roy. Stat. Soc. B (Stat. Methodol.) 61, 331–344 (1999) [CrossRef] [Google Scholar]
  38. M. Davidian, D.M. Giltinan, Nonlinear Models for Repeated Measurement Data, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, Taylor & Francis, 1995 [Google Scholar]
  39. R. Fisher, K. Pearson, On an Absolute Criterion for Fitting Frequency Curves. Messenger of Mathematics, 1911 [Google Scholar]
  40. D. Hall, M. Clutter, Multivariate multilevel nonlinear mixed-effects models for timber yield predictions, Biometrics 60, 16–24 (2004) [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  41. J. McFarland, S. Mahadevan, L. Romeroand, V. Swiler, Calibration and uncertainty analysis for computer simulations with multivariate output, AIAA J. 46, 1253–1265 (2008) [CrossRef] [Google Scholar]
  42. N. Metropolis, S. Ulam, The monte carlo method, J. Am. Stat. Assoc. 44, 335–341 (1949) [CrossRef] [PubMed] [Google Scholar]
  43. D. Bates, D. Hamilton, D. Watts, Calculation of intrinsic and parameter-effects curvatures for nonlinear regression models. Commun. Stat. Simul. Comput. 12, 469–477 (1983) [CrossRef] [Google Scholar]
  44. N. Hansen, S. Muller, P. Koumoutsakos, Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES), Evol. Comput. 11, 1–18 (2003) [CrossRef] [PubMed] [Google Scholar]
  45. W. Oberkampf, C. Roy, Verification and Validation in Scientific Computing, Cambridge University Press, 2010 [CrossRef] [Google Scholar]
  46. L. Schwer, Guide for Verification and Validation in Computational Solid Mechanics, ASME, 2006 [Google Scholar]
  47. B. Jung, H. Yoon, H. Oh, G. Lee, M. Yoo, B. Youn, Y. Hu. Hierarchical model calibration for designing piezoelectric energy harvester in the presence of variability in material properties and geometry, Struct. Multidiscipl. Optim. 53, 161–173 (2016) [CrossRef] [MathSciNet] [Google Scholar]
  48. B.D. Youn, B. Jung, Z. Xi, S.B. Kim, W.R. Lee, A hierarchical framework for statistical model calibration in engineering product development, Comput. Methods Appl. Mech. Eng. 200, 1421–1431 (2011) [CrossRef] [Google Scholar]
  49. A. Urbina, T. Paez, T. Consulting, A bayes network approach to uncertainty quantification in hierarchically-developed computational models, Int. J. Uncertain. Quantif. 2, 173–193 (2012) [CrossRef] [MathSciNet] [Google Scholar]
  50. J. Ye, M. Mahmoudi, K. Karayagiz, L. Johnson, R. Seede, Y. Karaman, R. Arroyave, A. Elwany, Bayesian calibration of multiple coupled simulation models for metal additive manufacturing: a Bayesian network approach. ASCE-ASME J. Risk Uncertain. Eng. Syst. B: Mech. Eng. 8, 011111 (2022) [Google Scholar]
  51. T. Kimand, B. Youn, Identifiability-based model decomposition for hierarchical Calibration, Struct. Multidiscipl. Optim. 60, 1801–1811 (2019) [Google Scholar]
  52. M. Sklar, Fonctions de repartition a n dimensions et leurs marges. in Annales de l’ISUP, Vol. 8, 1959, pp. 229–231 [Google Scholar]
  53. G. Gilbert, Positive definite matrices and sylvester’s criterion, Am. Math. Monthly 98, 44–46 (1991) [CrossRef] [Google Scholar]
  54. F. Mentre, A. Mallet, D. Baccar, Optimal design in random-effects regression models, Biometrika 84, 429–442 (1997) [CrossRef] [MathSciNet] [Google Scholar]
  55. C. Vong, S. Ueckert, J. Nyberg, A.C. Hooker, Handling below limit of quantification data in optimal trial design, J. Pharmacokinet. Pharmacodyn. (2014) [Google Scholar]
  56. F. Laurin, Approche Multiechelle des Mecanismes de Ruine Progressive des Materiaux Stratifies et Analyse de la Tenue de Structures Composites (in French) PhD thesis, 2005. [Google Scholar]
  57. J. Germain, Evaluation des capacites prédictives d’un modele avance pour la prevision de la tenue de plaques stratifiees perforées (in french), Theses, Universite Paris-Saclay, January 2020 [Google Scholar]
  58. I. Daniel, O. Ishai, Engineering Mechanics of Composite Materials, Oxford university press New York, 2006 [Google Scholar]
  59. J. Tomblinand, Y. Ng, S. Keshavanarayana, Material qualification and equivalency for polymer matrix composite material systems, Technical report, 2001 [Google Scholar]
  60. S. Kullback, R. A. Leibler, On information and sufficiency, Ann. Math. Stat. 22, 79–86 (1951) [CrossRef] [Google Scholar]
  61. L. Pardo, Statistical Inference based on Divergence Measures, CRC Press, 2018 [CrossRef] [Google Scholar]
  62. Y. Rollet, Vers une Maîtrise des Incertitudes en Calcul des Structures Composites (in French), PhD thesis, 2007 [Google Scholar]
  63. C.R. Harris, K. Jarrod Millman, S. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N.J. Smith, R. Kern, M. Picus, S. Hoyer, M.H. van Kerkwijk, M. Brett, A. Haldane, J. del Rio Fernandez, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, T.E. Oliphant, Array programming with NumPy, Nature 585, 357–362 (2020) [CrossRef] [PubMed] [Google Scholar]
  64. M. Baudin, A. Dutfoy, B. Iooss, A.-L. Popelin, Open-TURNS: an industrial software for uncertainty quantification in simulation. in R. Ghanem, D. Higdon, H. Owhadi (Eds.), Handbook of Uncertainty Quantification, Springer International Publishing, 2016, pp. 1–38 [Google Scholar]
  65. S.J. Johnson, The nlopt nonlinear-optimization package [Google Scholar]
  66. J. Pinheiro, D. Bates, Unconstrained parametrizations for variance=-covariance matrices. Stat. Comput. 6, 289–296 (1996) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.