On forward and inverse uncertainty quantification for models involving hysteresis operators
Mathematical modelling of natural phenomena, Tome 15 (2020), article no. 53.

Voir la notice de l'article provenant de la source EDP Sciences

Parameters within hysteresis operators modeling real world objects have to be identified from measurements and are therefore subject to corresponding errors. To investigate the influence of these errors, the methods of Uncertainty Quantification (UQ) are applied. Results of forward UQ for a play operator with a stochastic yield limit are presented. Moreover, inverse UQ is performed to identify the parameters in the weight function in a Prandtl-Ishlinskiĭ operator and the uncertainties of these parameters.
DOI : 10.1051/mmnp/2020009

Olaf Klein 1 ; Daniele Davino 2 ; Ciro Visone 3

1 Weierstrass Institute (WIAS), Mohrenstr. 39, 10117 Berlin, Germany.
2 Università degli Studi del Sannio, P.zza Roma, 21 - 82100 Benevento, Italy.
3 Università di Napoli Federico II, Via Claudio, 21- 80125 Napoli, Italy.
@article{MMNP_2020_15_a57,
     author = {Olaf Klein and Daniele Davino and Ciro Visone},
     title = {On forward and inverse uncertainty quantification for models involving hysteresis operators},
     journal = {Mathematical modelling of natural phenomena},
     eid = {53},
     publisher = {mathdoc},
     volume = {15},
     year = {2020},
     doi = {10.1051/mmnp/2020009},
     language = {en},
     url = {https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020009/}
}
TY  - JOUR
AU  - Olaf Klein
AU  - Daniele Davino
AU  - Ciro Visone
TI  - On forward and inverse uncertainty quantification for models involving hysteresis operators
JO  - Mathematical modelling of natural phenomena
PY  - 2020
VL  - 15
PB  - mathdoc
UR  - https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020009/
DO  - 10.1051/mmnp/2020009
LA  - en
ID  - MMNP_2020_15_a57
ER  - 
%0 Journal Article
%A Olaf Klein
%A Daniele Davino
%A Ciro Visone
%T On forward and inverse uncertainty quantification for models involving hysteresis operators
%J Mathematical modelling of natural phenomena
%D 2020
%V 15
%I mathdoc
%U https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020009/
%R 10.1051/mmnp/2020009
%G en
%F MMNP_2020_15_a57
Olaf Klein; Daniele Davino; Ciro Visone. On forward and inverse uncertainty quantification for models involving hysteresis operators. Mathematical modelling of natural phenomena, Tome 15 (2020), article  no. 53. doi : 10.1051/mmnp/2020009. https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2020009/

[1] M. Al Janaideh, C. Visone, D. Davino, P. Krejčí. The generalized Prandtl-Ishlinskii model: relation with the Preisach nonlinearity and inverse compensation error 2014 American Control Conference (ACC) June 4 -6, 2014. Portland, Oregon, USA 2014

[2] M. Al Janaideh, D. Davino, P. Krejčí, C. Visone Comparison of Prandtl–Ishlinskii and Preisach modeling for smart devices applications Phys. B: Condens. Matter 2016 155 159

[3] M. Brokate and J. Sprekels, Hysteresis and phase transitions. Springer, New York (1996).

[4] D. Davino, P. Krejčí, C. Visone Fully coupled modeling of magneto-mechanical hysteresis through ‘thermodynamic’ compatibility Smart Mater. Struct. 2013 9

[5] D. Davino, C. Visone Rate-independent memory in magneto-elastic materials Discrete Continuous Dyn. Syst. Ser. S 2015 649 691

[6] O. Klein, P. Krejčí Outwards pointing hysteresis operators and asymptotic behaviour of evolution equations Nonlinear Anal. Real World Appl. 2003 755 785

[7] O. Klein, P. Krejčí Asymptotic behaviour of evolution equations involving outwards pointing hysteresis operators Phys. B 2004 53 58

[8] M. Krasnosel’skiǐ and A. Pokrovskii, Systems with Hysteresis. Russian edition: Nauka, Moscow, 1983. Springer-Verlag, Heidelberg (1989).

[9] P. Krejčí, Hysteresis, Convexity and Dissipation in Hyperbolic Equations, Vol. 8 of Gakuto Int. Series Math. Sci. Appl. Gakkōtosho, Tokyo (1996).

[10] W. Liu, A Geostatistical Approach toward Shear Wave Velocity Modeling and Uncertainty Quantification in Seismic Hazard. Dissertations, Clemson University (2018).

[11] S.F. Masri, R. Ghanem, F. Arrate, J. Caffrey Stochastic nonparametric models of uncertain hysteretic oscillators AIAA J. 2006 2319 2330

[12] I.D. Mayergoyz, Mathematical Models of Hysteresis and their Applications. 2nd edn. Elsevier, Amsterdam (2003).

[13] D.D. Rizos, S.D. Fassois A-posteriori identifiability of the maxwell slip model of hysteresis IFAC Proc. 2011 10788 10793

[14] R.C. Smith, Uncertainty quantification: theory, implementation, and applications, Vol. 12 of Computational Science Engineering. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA (2014).

[15] T.J. Sullivan, Introduction to uncertainty quantification, Vol. 63 of Texts in Applied Mathematics. Springer, Cham (2015).

[16] S.P. Triantafyllou, E.N. Chatzi A hysteretic multiscale formulation for validating computational models of heterogeneous structures J. Strain Anal. Eng. Des. 2015 46 62

[17] A. Visintin, Differential Models of Hysteresis, Vol. 111 of Applied Mathematical Sciences. Springer, New York (1994).

[18] C. Visone, M. Sjöström Exact invertible hysteresis models based on play operators Phys. B: Condens. Matter 2004 148 152

[19] Y. Zhang Stochastic responses of multi-degree-of-freedom uncertain hysteretic systems 2011

Cité par Sources :