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Title: On forward and inverse uncertainty quantification for a model for a magneto mechanical device involving a hysteresis operator (English)
Author: Klein, Olaf
Language: English
Journal: Applications of Mathematics
ISSN: 0862-7940 (print)
ISSN: 1572-9109 (online)
Volume: 68
Issue: 6
Year: 2023
Pages: 795-828
Summary lang: English
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Category: math
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Summary: Modeling real world objects and processes one may have to deal with hysteresis effects but also with uncertainties. Following D. Davino, P. Krejčí, and C. Visone (2013), a model for a magnetostrictive material involving a generalized Prandtl-Ishlinski\u ı-operator is considered here. \endgraf Using results of measurements, some parameters in the model are determined and inverse Uncertainty Quantification (UQ) is used to determine random densities to describe the remaining parameters and their uncertainties. Afterwards, the results are used to perform forward UQ and to compare the generated outputs with measured data. This extends some of the results from O. Klein, D. Davino, and C. Visone (2020). (English)
Keyword: hysteresis
Keyword: uncertainty quantification (UQ)
Keyword: magnetostrictive material
Keyword: Bayesian inverse problems (BIP)
MSC: 47J40
MSC: 60H30
idZBL: Zbl 07790546
idMR: MR4669930
DOI: 10.21136/AM.2023.0080-23
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Date available: 2023-11-23T12:15:09Z
Last updated: 2024-12-13
Stable URL: http://hdl.handle.net/10338.dmlcz/151940
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Reference: [1] Janaideh, M. Al, Visone, C., Davino, D., Krejčí, P.: The generalized Prandtl-Ishlinskii model: Relation with the Preisach nonlinearity and inverse compensation error.American Control Conference (ACC) Portland, Oregon (2014), 4759-4764. 10.1109/ACC.2014.6858952
Reference: [2] Brokate, M., Sprekels, J.: Hysteresis and Phase Transitions.Applied Mathematical Sciences 121. Springer, New York (1996). Zbl 0951.74002, MR 1411908, 10.1007/978-1-4612-4048-8
Reference: [3] Davino, D., Krejčí, P., Visone, C.: Fully coupled modeling of magneto-mechanical hysteresis through `thermodynamic' compatibility.Smart Mater. Struct. 22 (2013), Article ID 095009. 10.1088/0964-1726/22/9/095009
Reference: [4] Davino, D., Visone, C.: Rate-independent memory in magneto-elastic materials.Discrete Contin. Dyn. Syst., Ser. S 8 (2015), 649-691. Zbl 1302.93028, MR 3356455, 10.3934/dcdss.2015.8.649
Reference: [5] Grimmett, G., Stirzaker, D.: Probability and Random Processes.Oxford University Press, New York (2001). Zbl 1015.60002, MR 2059709
Reference: [6] Kaipio, J., Somersalo, E.: Statistical and Computational Inverse Problems.Applied Mathematical Sciences 160. Springer, New York (2005). Zbl 1068.65022, MR 2102218, 10.1007/b138659
Reference: [7] Klein, O., Davino, D., Visone, C.: On forward and inverse uncertainty quantification for models involving hysteresis operators.Math. Model. Nat. Phenom. 15 (2020), Article ID 53, 19 pages. Zbl 07372583, MR 4175380, 10.1051/mmnp/2020009
Reference: [8] Klein, O., Krejčí, P.: Outwards pointing hysteresis operators and asymptotic behaviour of evolution equations.Nonlinear Anal., Real World Appl. 4 (2003), 755-785. Zbl 1043.47047, MR 1978561, 10.1016/S1468-1218(03)00013-0
Reference: [9] Krasnosel'skij, M. A., Pokrovskij, A. V.: Systems with Hysteresis.Springer, Berlin (1989). Zbl 0665.47038, MR 0987431, 10.1007/978-3-642-61302-9
Reference: [10] Krejčí, P.: Hysteresis, Convexity and Dissipation in Hyperbolic Equations.GAKUTO International Series. Mathematical Sciences and Applications 8. Gakkotosho, Tokyo (1996). Zbl 1187.35003, MR 2466538
Reference: [11] Krejčí, P.: Long-time behavior of solutions to hyperbolic equations with hysteresis.Evolutionary Equations. Vol. II Handbook of Differential Equations. Elsevier, Amsterdam (2005), 303-370. Zbl 1098.35030, MR 2182830, 10.1016/S1874-5717(06)80007-1
Reference: [12] Lee, P. M.: Bayesian Statistics: An Introduction.John Wiley & Sons, Chichester (2012). Zbl 1258.62028, MR 3237439
Reference: [13] Marelli, S., Sudret, B.: UQLab: A framework for uncertainty quantification in Matlab.Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management American Society of Civil Engineers, Reston (2014). 10.1061/9780784413609.257
Reference: [14] Mayergoyz, I. D.: Mathematical Models of Hysteresis.Springer, New York (1991). Zbl 0723.73003, MR 1083150, 10.1007/978-1-4612-3028-1
Reference: [15] Moustapha, M., Lataniotis, C., Wiederkehr, P., Wagner, P.-R., Wicaksono, D., Marelli, S., Sudret, B.: UQLib User Manual.ETH, Zürich (2022), Available at https://www.uqlab.com/uqlib-user-manual\kern0pt.
Reference: [16] Pawitan, Y.: In All Likelihood: Statistical Modelling and Inference Using Likelihood.Oxford Science Publications. Oxford University Press, Oxford (2001). Zbl 1013.62001
Reference: [17] Smith, R. C.: Uncertainty Quantification: Theory, Implementation, and Applications.Computational Science & Engineering 12. SIAM, Philadelphia (2014). Zbl 1284.65019, MR 3155184, 10.1137/1.9781611973228
Reference: [18] Sullivan, T. J.: Introduction to Uncertainty Quantification.Texts in Applied Mathematics 63. Springer, Cham (2015). Zbl 1336.60002, MR 3364576, 10.1007/978-3-319-23395-6
Reference: [19] Visintin, A.: Differential Models of Hysteresis.Applied Mathematical Sciences 111. Springer, Berlin (1994). Zbl 0820.35004, MR 1329094, 10.1007/978-3-662-11557-2
Reference: [20] Visone, C., Sjöström, M.: Exact invertible hysteresis models based on play operators.Phys. B, Condens. Matter 343 (2004), 148-152. 10.1016/j.physb.2003.08.087
Reference: [21] Wagner, P.-R., Nagel, J., Marelli, S., Sudret, B.: UQLab User Manual: Bayesian Inversion for Model Calibration and Validation.ETH, Zürich (2022), Available at https://www.uqlab.com/inversion-user-manual\kern0pt.
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