REFERENCES
1. Cai J, Hu S, Sun F, Tang L, Fan G, Xing H. Exploring the relationship between risk perception and public disaster mitigation behavior in geological hazard emergency management: a research study in Wenchuan county. Dis Prev Res 2023;2:21.
2. Du Y, Xu J. Structural reliability analysis with epistemic and aleatory uncertainties via AK-MCS with a new learning function. Dis Prev Res 2023;2:15.
3. De Santis Y, Aloisio A, Pasca DP, Fragiacomo M, Dombrowski F. Evaluation of the shear size effect in glued laminated timber using a stochastic FE model calibrated on 17000 glue-line tests. Constr Build Mater 2023;399:132488.
4. Ghosh D, Avery P, Farhat C. A FETI-preconditioned conjugate gradient method for large-scale stochastic finite element problems. Int J Numer Meth Eng 2009;80:914-31.
5. Zhang R, Dai H. Stochastic analysis of structures under limited observations using kernel density estimation and arbitrary polynomial chaos expansion. Comput Method Appl Mech Eng 2023;403:115689.
6. Wang Y, Li J. Probabilistic analysis on stochastic failure modes of reinforced concrete beams under fatigue loading. J Eng Mech 2022;148:04022018.
7. Zheng Z, Dai H, Beer M. Efficient structural reliability analysis via a weak-intrusive stochastic finite element method. Probabilist Eng Mech 2023;71:103414.
8. Sousedík B, Ghanem RG, Phipps ET. Hierarchical Schur complement preconditioner for the stochastic Galerkin finite element methods. Numer Linear Algebra Appl 2014;21:136-51.
9. Peng Y, Zhou T, Li J. Surrogate modeling immersed probability density evolution method for structural reliability analysis in high dimensions. Mech Syst Signal Proc 2021;152:107366.
10. Dai H, Zhang R, Beer M. A new method for stochastic analysis of structures under limited observations. Mech Syst Signal Proc 2023;185:109730.
11. Pang R, Zhou Y, Chen G, Jing M, Yang D. Stochastic mainshock-aftershock simulation and its applications in dynamic reliability of structural systems via DPIM. J Eng Mech 2023;149:04022096.
12. He J, Chen J, Ren X, Li J. Uncertainty quantification of random fields based on spatially sparse data by synthesizing bayesian compressive sensing and stochastic harmonic function. Mech Syst Signal Proc 2021;153:107377.
13. Pranesh S, Ghosh D. Addressing the curse of dimensionality in SSFEM using the dependence of eigenvalues in KL expansion on domain size. Comput Method Appl Mech Eng 2016;311:457-75.
14. Meng Z, Zhao J, Chen G, Yang D. Hybrid uncertainty propagation and reliability analysis using direct probability integral method and exponential convex model. Reliab Eng Syst Safe 2022;228:108803.
15. Kundu A, Diazdelao F, Adhikari S, Friswell M. A hybrid spectral and metamodeling approach for the stochastic finite element analysis of structural dynamic systems. Comput Method Appl Mech Eng 2014;270:201-19.
16. Wan Z, Chen J, Li J. Probability density evolution analysis of stochastic seismic response of structures with dependent random parameters. Probabilist Eng Mech 2020;59:103032.
17. Mouyeaux A, Carvajal C, Bressolette P, Peyras L, Breul P, Bacconnet C. Probabilistic stability analysis of an earth dam by stochastic finite element method based on field data. Comput Geotech 2018;101:34-47.
18. Zheng Z, Dai H. Structural stochastic responses determination via a sample-based stochastic finite element method. Comput Method Appl Mech Eng 2021;381:113824.
19. Xiu D. Numerical methods for stochastic computations: a spectral method approach Princeton, NJ: Princeton University Press; 2010.
20. Stefanou G. The stochastic finite element method: past, present and future. Comput Method Appl Mech Eng 2008;198:1031-51.
21. Matthies HG, Keese A. Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations. Comput Method Appl Mech Eng 2005;194:1295-331.
22. Ghanem RG, Spanos PD. Stochastic finite elements: a spectral approach. New York Berlin Heidelberg: Springer; 1991.
23. Gopalakrishnan S, Chakraborty A, Mahapatra DR. Spectral finite element method. London: Springer; 2012.
24. Dai H, Zheng Z, Ma H. An explicit method for simulating non-Gaussian and non-stationary stochastic processes by Karhunen-Loeve and polynomial chaos expansion. Mech Syst Signal Proc 2019;115:1-13.
25. Stavroulakis G, Giovanis DG, Papadopoulos V, Papadrakakis M. A GPU domain decomposition solution for spectral stochastic finite element method. Comput Method Appl Mech Eng 2017;327:392-410.
26. Farhat C, Wilson E, Powell G. Solution of finite element systems on concurrent processing computers. Eng Comput 1987;2:157-65.
27. Farhat C, Lesoinne M, Pierson K. A scalable dual-primal domain decomposition method. Numer Linear Algebra Appl 2000;7:687-714.
28. Sarkar A, Benabbou N, Ghanem R. Domain decomposition of stochastic PDEs: theoretical formulations. Int J Numer Methods Eng 2009;77:689-701.
29. Cros JM. A preconditioner for the Schur complement domain decomposition method. 2003. Available from: http://www.ddm.org/DD14/cros.pdf[Last accessed on 21 Mar 2024.
30. Desai A, Khalil M, Pettit C, Poirel D, Sarkar A. Scalable domain decomposition solvers for stochastic PDEs in high performance computing. Comput Method Appl Mech Eng 2018;335:194-222.
31. Subber W, Sarkar A. A domain decomposition method of stochastic PDEs: An iterative solution techniques using a two-level scalable preconditioner. J Comput Phys 2014;257:298-317.
32. Toselli A, Widlund OB. Domain decomposition methods-algorithms and theory. In Springer series in computational mathematics, Berlin: Springer; 2005.
33. Pranesh S, Ghosh D. A FETI-DP based parallel hybrid stochastic finite element method for large stochastic systems. Comput Struct 2018;195:64-73.
34. Mathew TPA. Domain decomposition methods for the numerical solution of partial differential equations. Berlin: Springer; 2008.
36. Babuska I, Tempone R, Zouraris GE. Galerkin finite element approximations of stochastic elliptic partial differential equations. SIAM J Numer Anal 2004;42:800-25.
37. Ullmann E. A kronecker product preconditioner for stochastic galerkin finite element discretizations. SIAM J Sci Comput 2010;32:923-46.
