Theory of Probability and Mathematical Statistics
Finite dimensional models for random microstructures
M. Grigoriu
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Abstract: Finite dimensional (FD) models, i.e., deterministic functions of space depending on finite sets of random variables, are used extensively in applications to generate samples of random fields Z(x) and construct approximations of solutions U(x) of ordinary or partial differential equations whose random coefficients depend on Z(x). FD models of Z(x) and U(x) constitute surrogates of these random fields which target various properties, e.g., mean/correlation functions or sample properties. We establish conditions under which samples of FD models can be used as substitutes for samples of Z(x) and U(x) for two types of random fields Z(x) and a simple stochastic equation. Some of these conditions are illustrated by numerical examples.
Keywords: Material microstructures, random fields, space of continuous functions, stochastic equations, weak/almost sure convergence
Bibliography: 1. R. J. Adler, The geometry of random fields, Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons, Ltd., Chichester, 1981. MR 634676
2. P. Billingsley, Convergence of probability measures, John Wiley & Sons, Inc., New York, 1968. MR 1700749
3. P.-L. Chow, Stochastic partial differential equations, Chapman & Hall/CRC Applied Mathematics and Nonlinear Science Series, Chapman & Hall/CRC, Boca Raton, FL, 2007. MR 2295103
4. H. Cramér and M. R. Leadbetter, Stationary and related stochastic processes, reprint of the 1967 original, Dover Publications, Inc., Mineola, NY, 2004. MR 2108670
5. W. B. Davenport. Probability and random processes, McGraw-Hill Book Company, New York, 1970.
6. I. Gohberg and S. Goldberg, Basic operator theory, Birkhäuser, Boston, MA, 1981. MR 632943
7. M. Grigoriu, Applied Non-Gaussian Processes: Examples, Theory, Simulation, Linear Random Vibration, and MATLAB Solutions, Prentice Hall, Englewoods Cliffs, NJ, 1995. MR 0119047
8. M. Grigoriu, Stochastic calculus. Applications in Science and Engineering, Birkhäuser Boston, Inc., Boston, MA, 2002. MR 1926011
9. M. Grigoriu, Existence and construction of translation models for stationary non-Gaussian processes, Probabilistic Engineering Mechanics, 24:545–551, 2009.
10. M. Grigoriu, Finite dimensional models for random functions, J. Comput. Phys. 376 (2019), 1253–1272. MR 3875566
11. E. J. Hannan, Multiple time series, John Wiley & Sons, Inc., New York, 1970. MR 0279952
12. D. B. Hernández, Lectures on probability and second order random fields, Series on Advances in Mathematics for Applied Sciences, 30, World Scientific Publishing Co., Inc., River Edge, NJ, 1995. MR 1412573
13. K. Itô and M. Nisio, On the convergence of sums of independent Banach space valued random variables, Osaka Math. J. 5 (1968), 35–48. MR 235593
14. S. Kwapień and W. A. Woyczyński, Random series and stochastic integrals: single and multiple, Probability and its Applications, Birkhäuser Boston, Inc., Boston, MA, 1992. MR 1167198
15. M. Ostoja-Starzewski, Microstructural randomness and scaling in mechanics of materials, CRC Series: Modern Mechanics and Mathematics, Chapman & Hall/CRC, Boca Raton, FL, 2008. MR 2341287
16. M. Rosenblatt, Random processes, second edition, Graduate Texts in Mathematics, No. 17, Springer-Verlag, New York, 1974. MR 0346883
17. J. Shao, Mathematical statistics, second edition, Springer Texts in Statistics, Springer-Verlag, New York, 2003. MR 2002723
18. C. Soize, Non-Gaussian positive-definite matrix-valued random fields for elliptic stochastic partial differential operators, Comput. Methods Appl. Mech. Engrg. 195 (2006), no. 1-3, 26–64. MR 2174357
19. G. P. Tolstov, Fourier series, Dover Publications, Inc., New York, 1976. MR 0425474
20. A. W. van der Vaart, Asymptotic statistics, Cambridge Series in Statistical and Probabilistic Mathematics, 3, Cambridge University Press, Cambridge, 1998. MR 1652247
21. A. M. Yaglom, An introduction to the theory of stationary random functions, Prentice-Hall, Inc., Englewood Cliffs, NJ, 1962. MR 0184289