Name
Abstract | ||
|---|---|---|
A new class of parametric models, referred to as sample parametric models, is developed for random elements that match sample rather than the first two moments and/or other global properties of these elements. The models can be used to characterize, e.g., material properties at small scale in which case their samples represent microstructures of material specimens selected at random from a population. The samples of the proposed models are elements of finite-dimensional vector spaces spanned by samples, eigenfunctions of Karhunen-Loève (KL) representations, or modes of singular value decompositions (SVDs). The implementation of sample parametric models requires knowledge of the probability laws of target random elements. Numerical examples including stochastic processes and random fields are used to demonstrate the construction of sample parametric models, assess their accuracy, and illustrate how these models can be used to solve efficiently stochastic equations. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.jcp.2015.04.053 | Journal of Computational Physics |
Keywords | Field | DocType |
Karhunen–Loève expansion,Monte Carlo simulation,Parametric models,Random elements,Sample properties,Singular value decomposition | Singular value decomposition,Population,Vector space,Mathematical optimization,Monte Carlo method,Random field,Parametric model,Singular value,Stochastic process,Mathematics | Journal |
Volume | Issue | ISSN |
297 | C | 0021-9991 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mircea Grigoriu | 1 | 4 | 3.83 |