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Title: A comparison of deterministic and Bayesian inverse with application in micromechanics (English)
Author: Blaheta, Radim
Author: Béreš, Michal
Author: Domesová, Simona
Author: Pan, Pengzhi
Language: English
Journal: Applications of Mathematics
ISSN: 0862-7940 (print)
ISSN: 1572-9109 (online)
Volume: 63
Issue: 6
Year: 2018
Pages: 665-686
Summary lang: English
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Category: math
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Summary: The paper deals with formulation and numerical solution of problems of identification of material parameters for continuum mechanics problems in domains with heterogeneous microstructure. Due to a restricted number of measurements of quantities related to physical processes, we assume additional information about the microstructure geometry provided by CT scan or similar analysis. The inverse problems use output least squares cost functionals with values obtained from averages of state problem quantities over parts of the boundary and Tikhonov regularization. To include uncertainties in observed values, Bayesian inversion is also considered in order to obtain a statistical description of unknown material parameters from sampling provided by the Metropolis-Hastings algorithm accelerated by using the stochastic Galerkin method. The connection between Bayesian inversion and Tikhonov regularization and advantages of each approach are also discussed. (English)
Keyword: inverse problems
Keyword: Bayesian approach
Keyword: stochastic Galerkin method
MSC: 60-08
MSC: 65C60
MSC: 65N21
MSC: 82-08
MSC: 86-08
idZBL: Zbl 07031682
idMR: MR3893005
DOI: 10.21136/AM.2018.0195-18
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Date available: 2019-01-03T09:11:04Z
Last updated: 2021-01-04
Stable URL: http://hdl.handle.net/10338.dmlcz/147563
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Reference: [1] Babuška, I., Tempone, R., Zouraris, G. E.: Galerkin finite element approximations of Stochastic elliptic partial differential equations.SIAM J. Numer. Anal. 42 (2004), 800-825. Zbl 1080.65003, MR 2084236, 10.1137/S0036142902418680
Reference: [2] Béreš, M., Domesová, S.: The stochastic Galerkin method for Darcy flow problem with log-normal random field coefficients.Adv. Electr. Electron. Eng. 15 (2017), 267-279. 10.15598/aeee.v15i2.2280
Reference: [3] Blaheta, R., Béreš, M., Domesová, S.: A study of stochastic FEM method for porous media flow problem.Proc. Int. Conf. Applied Mathematics in Engineering and Reliability CRC Press (2016), 281-289. 10.1201/b21348-47
Reference: [4] Blaheta, R., Kohut, R., Kolcun, A., Souček, K., Staš, L., Vavro, L.: Digital image based numerical micromechanics of geocomposites with application to chemical grouting.Int. J. Rock Mechanics and Mining Sciences 77 (2015), 77-88. 10.1016/j.ijrmms.2015.03.012
Reference: [5] Boffi, D., Brezzi, F., Fortin, M.: Mixed Finite Element Methods and Applications.Springer Series in Computational Mathematics 44, Springer, Berlin (2013). Zbl 1277.65092, MR 3097958, 10.1007/978-3-642-36519-5
Reference: [6] Carey, G. F., Chow, S. S., Seager, M. K.: Approximate boundary-flux calculations.Comput. Methods Appl. Mech. Eng. 50 (1985), 107-120. Zbl 0546.73057, MR 0802335, 10.1016/0045-7825(85)90085-4
Reference: [7] Christen, J. A., Fox, C.: Markov chain Monte Carlo using an approximation.J. Comput. Graph. Statist. 14 (2005), 795-810. MR 2211367, 10.1198/106186005X76983
Reference: [8] Domesová, S., Béreš, M.: Inverse problem solution using Bayesian approach with application to Darcy flow material parameters estimation.Adv. Electr. Electron. Eng. 15 (2017), 258-266. 10.15598/aeee.v15i2.2236
Reference: [9] Domesová, S., Béreš, M.: A Bayesian approach to the identification problem with given material interfaces in the Darcy flow.Int. Conf. High Performance Computing in Science and Engineering, 2017 T. Kozubek et al. Springer International Publishing, Cham (2018), 203-216. 10.1007/978-3-319-97136-0_15
Reference: [10] Foreman-Mackey, D., Hogg, D. W., Lang, D., Goodman, J.: emcee: The MCMC hammer.Publ. Astron. Soc. Pacific 125 (2013), 306-312. 10.1086/670067
Reference: [11] Gatica, G. N.: A Simple Introduction to the Mixed Finite Element Method. Theory and Applications.SpringerBriefs in Mathematics, Springer, Cham (2014). Zbl 1293.65152, MR 3157367, 10.1007/978-3-319-03695-3
Reference: [12] Haslinger, J., Blaheta, R., Hrtus, R.: Identification problems with given material interfaces.J. Comput. Appl. Math. 310 (2017), 129-142. Zbl 1347.49052, MR 3544595, 10.1016/j.cam.2016.06.023
Reference: [13] Lord, G. J., Powell, C. E., Shardlow, T.: An Introduction to Computational Stochastic PDEs.Cambridge Texts in Applied Mathematics, Cambridge University Press, Cambridge (2014). Zbl 1327.60011, MR 3308418, 10.1017/CBO9781139017329
Reference: [14] Mathworks: Matlab Optimization Toolbox User's Guide.Available at https://uk.mathworks.com/products/optimization.html (2017).
Reference: [15] Powell, C. E., Silvester, D., Simoncini, V.: An efficient reduced basis solver for Stochastic Galerkin matrix equations.SIAM J. Sci. Comput. 39 (2017), A141--A163. Zbl 1381.35257, MR 3594329, 10.1137/15M1032399
Reference: [16] Pultarová, I.: Hierarchical preconditioning for the stochastic Galerkin method: Upper bounds to the strengthened CBS constants.Comput. Math. Appl. 71 (2016), 949-964. MR 3461271, 10.1016/j.camwa.2016.01.006
Reference: [17] Robert, C. P.: The Bayesian Choice. From Decision-Theoretic Foundations to Computational Implementation.Springer Texts in Statistics, Springer, New York (2007). Zbl 1129.62003, MR 2723361, 10.1007/0-387-71599-1
Reference: [18] Robert, C. P., Casella, G.: Monte Carlo Statistical Methods.Springer Texts in Statistics, Springer, New York (2004). Zbl 1096.62003, MR 2080278, 10.1007/978-1-4757-4145-2
Reference: [19] Sokal, A.: Monte Carlo methods in statistical mechanics: Foundations and new algorithms.Functional Integration: Basics and Applications, 1996 C. DeWitt-Morette et al. NATO ASI Series. Series B. Physics. 361, Plenum Press, New York (1997), 131-192. Zbl 0890.65006, MR 1477456, 10.1007/978-1-4899-0319-8_6
Reference: [20] Stuart, A. M.: Inverse problems: A Bayesian perspective.Acta Numerica 19 (2010) 451-559. Zbl 1242.65142, MR 2652785, 10.1017/S0962492910000061
Reference: [21] Thompson, M. B.: A comparison of methods for computing autocorrelation time.Available at https://arxiv.org/abs/1011.0175 (2010).
Reference: [22] Vogel, C. R.: Computational Methods for Inverse Problems.Frontiers in Applied Mathematics 23, Society for Industrial and Applied Mathematics, Philadelphia (2002). Zbl 1008.65103, MR 1928831, 10.1137/1.9780898717570
Reference: [23] Xiu, D.: Numerical Methods for Stochastic Computations. A Spectral Method Approach.Princeton University Press, Princeton (2010). Zbl 1210.65002, MR 2723020, 10.2307/j.ctv7h0skv
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