Applications of Mathematics, Vol. 63, No. 6, pp. 665-686, 2018
A comparison of deterministic and Bayesian inverse with application in micromechanics
Radim Blaheta, Michal Béreš, Simona Domesová, Pengzhi Pan
Received July 16, 2018. Published online November 19, 2018.
Abstract: 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.
References: [1] I. Babuška, R. Tempone, G. E. Zouraris: Galerkin finite element approximations of Stochastic elliptic partial differential equations. SIAM J. Numer. Anal. 42 (2004), 800-825. DOI 10.1137/S0036142902418680 | MR 2084236 | Zbl 1080.65003
[2] M. Béreš, S. Domesová: The stochastic Galerkin method for Darcy flow problem with log-normal random field coefficients. Adv. Electr. Electron. Eng. 15 (2017), 267-279. DOI 10.15598/aeee.v15i2.2280
[3] R. Blaheta, M. Béreš, S. Domesová: A study of stochastic FEM method for porous media flow problem. Proc. Int. Conf. Applied Mathematics in Engineering and Reliability. CRC Press, 2016, pp. 281-289. DOI 10.1201/b21348-47
[4] R. Blaheta, R. Kohut, A. Kolcun, K. Souček, L. Staš, L. Vavro: Digital image based numerical micromechanics of geocomposites with application to chemical grouting. Int. J. Rock Mechanics and Mining Sciences 77 (2015), 77-88. DOI 10.1016/j.ijrmms.2015.03.012
[5] D. Boffi, F. Brezzi, M. Fortin: Mixed Finite Element Methods and Applications. Springer Series in Computational Mathematics 44, Springer, Berlin (2013). DOI 10.1007/978-3-642-36519-5 | MR 3097958 | Zbl 1277.65092
[6] G. F. Carey, S. S. Chow, M. K. Seager: Approximate boundary-flux calculations. Comput. Methods Appl. Mech. Eng. 50 (1985), 107-120. DOI 10.1016/0045-7825(85)90085-4 | MR 0802335 | Zbl 0546.73057
[7] J. A. Christen, C. Fox: Markov chain Monte Carlo using an approximation. J. Comput. Graph. Statist. 14 (2005), 795-810. DOI 10.1198/106186005X76983 | MR 2211367
[8] S. Domesová, M. Béreš: Inverse problem solution using Bayesian approach with application to Darcy flow material parameters estimation. Adv. Electr. Electron. Eng. 15 (2017), 258-266. DOI 10.15598/aeee.v15i2.2236
[9] S. Domesová, M. Béreš: 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., eds.). Springer International Publishing, Cham, 2018, pp. 203-216. DOI 10.1007/978-3-319-97136-0_15
[10] D. Foreman-Mackey, D. W. Hogg, D. Lang, J. Goodman: emcee: The MCMC hammer. Publ. Astron. Soc. Pacific 125 (2013), 306-312. DOI 10.1086/670067
[11] G. N. Gatica: A Simple Introduction to the Mixed Finite Element Method. Theory and Applications. SpringerBriefs in Mathematics, Springer, Cham (2014). DOI 10.1007/978-3-319-03695-3 | MR 3157367 | Zbl 1293.65152
[12] J. Haslinger, R. Blaheta, R. Hrtus: Identification problems with given material interfaces. J. Comput. Appl. Math. 310 (2017), 129-142. DOI 10.1016/j.cam.2016.06.023 | MR 3544595 | Zbl 1347.49052
[13] G. J. Lord, C. E. Powell, T. Shardlow: An Introduction to Computational Stochastic PDEs. Cambridge Texts in Applied Mathematics, Cambridge University Press, Cambridge (2014). DOI 10.1017/CBO9781139017329 | MR 3308418 | Zbl 1327.60011
[14] Mathworks: Matlab Optimization Toolbox User's Guide. Available at https://uk.mathworks.com/products/optimization.html (2017). sw 10828
[15] C. E. Powell, D. Silvester, V. Simoncini: An efficient reduced basis solver for Stochastic Galerkin matrix equations. SIAM J. Sci. Comput. 39 (2017), A141-A163. DOI 10.1137/15M1032399 | MR 3594329 | Zbl 1381.35257
[16] I. Pultarová: Hierarchical preconditioning for the stochastic Galerkin method: Upper bounds to the strengthened CBS constants. Comput. Math. Appl. 71 (2016), 949-964. DOI 10.1016/j.camwa.2016.01.006 | MR 3461271
[17] C. P. Robert: The Bayesian Choice. From Decision-Theoretic Foundations to Computational Implementation. Springer Texts in Statistics, Springer, New York (2007). DOI 10.1007/0-387-71599-1 | MR 2723361 | Zbl 1129.62003
[18] C. P. Robert, G. Casella: Monte Carlo Statistical Methods. Springer Texts in Statistics, Springer, New York (2004). DOI 10.1007/978-1-4757-4145-2 | MR 2080278 | Zbl 1096.62003
[19] A. Sokal: Monte Carlo methods in statistical mechanics: Foundations and new algorithms. Functional Integration: Basics and Applications, 1996 (C. DeWitt-Morette et al., eds.). NATO ASI Series. Series B. Physics. 361, Plenum Press, New York, 1997, pp. 131-192. DOI 10.1007/978-1-4899-0319-8_6 | MR 1477456 | Zbl 0890.65006
[20] A. M. Stuart: Inverse problems: A Bayesian perspective. Acta Numerica 19 (2010) 451-559. DOI 10.1017/S0962492910000061 | MR 2652785 | Zbl 1242.65142
[21] M. B. Thompson: A comparison of methods for computing autocorrelation time. Available at https://arxiv.org/abs/1011.0175 (2010).
[22] C. R. Vogel: Computational Methods for Inverse Problems. Frontiers in Applied Mathematics 23, Society for Industrial and Applied Mathematics, Philadelphia (2002). DOI 10.1137/1.9780898717570 | MR 1928831 | Zbl 1008.65103
[23] D. Xiu: Numerical Methods for Stochastic Computations. A Spectral Method Approach. Princeton University Press, Princeton (2010). DOI 10.2307/j.ctv7h0skv | MR 2723020 | Zbl 1210.65002
Affiliations: Radim Blaheta, Institute of Geonics of the CAS, Studentská 1768, 708 00 Ostrava, Czech Republic, e-mail: radim.blaheta@ugn.cas.cz; Michal Béreš, Simona Domesová, Institute of Geonics of the CAS, Studentská 1768, 708 00 Ostrava, Czech Republic; Department of Applied Mathematics, Faculty of Electrical Engineering and Computer Science, VŠB - Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava, Czech Republic; IT4Innovations National Supercomputing Center, VŠB - Technical University of Ostrava, Studentská 6231/1B, 708 33 Ostrava, Czech Republic, e-mail: michal.beres@vsb.cz, simona.domesova@vsb.cz; Pengzhi Pan, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Xiaohongshan, Wuchang, Wuhan 430071, China, e-mail: pzpan@whrsm.ac.cn