Uncertainty Quantification for Multiscale Simulations1

Author:

DeVolder B.1,Glimm J.23,Grove J. W.1,Kang Y.2,Lee Y.2,Pao K.1,Sharp D. H.1,Ye K.2

Affiliation:

1. Los Alamos National Laboratory, Los Alamos, NM 87545

2. Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794-3600

3. Center for Data Intensive Computing, Brookhaven National Laboratory, Upton, NY 11973

Abstract

A general discussion of the quantification of uncertainty in numerical simulations is presented. A principal conclusion is that the distribution of solution errors is the leading term in the assessment of the validity of a simulation and its associated uncertainty in the Bayesian framework. Key issues that arise in uncertainty quantification are discussed for two examples drawn from shock wave physics and modeling of petroleum reservoirs. Solution error models, confidence intervals and Gaussian error statistics based on simulation studies are presented.

Publisher

ASME International

Subject

Mechanical Engineering

Reference43 articles.

1. S. French and J. Q. Smith, eds., 1997, The Practice of Bayesian Analysis, Arnold, London.

2. Hadamard, J. , 1936, “Equations aux de´rive´es partielles. les conditions de´finies en ge´ne´ral. le cas hyperboliqaue,” Enseignement Math., 35, pp. 5–42.

3. R. Courant and D. Hilbert, 1962, Methods of Mathematical Physics II. Interscience, New York.

4. J. Glimm and D. H. Sharp, 1997, “Stochastic partial differential equations: Selected applications in continuum physics,” R. A. Carmona and B. L. Rozovskii, eds., Stochastic Partial Differential Equations: Six Perspectives, Mathematical Surveys and Monographs, American Mathematical Society, Providence.

5. Glimm, J., and Sharp, D. H., 1997, “Multiscale science,” SIAM News, Oct.

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