Sequential Design of Experiment for Sparse Polynomial Chaos Expansions
| Autor: | Stefano Marelli, Bruno Sudret, Noura Fajraoui |
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| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Mathematical optimization Applied Mathematics Sampling (statistics) Context (language use) Sample (statistics) 010103 numerical & computational mathematics 01 natural sciences Metamodeling 010104 statistics & probability Surrogate model Sequential analysis Modeling and Simulation Discrete Mathematics and Combinatorics Minification 0101 mathematics Statistics Probability and Uncertainty Uncertainty quantification Mathematics |
| Zdroj: | SIAM/ASA Journal on Uncertainty Quantification. 5:1061-1085 |
| ISSN: | 2166-2525 |
| Popis: | Uncertainty quantification (UQ) has received much attention in the literature in the past decade. In this context, sparse polynomial chaos expansions (PCEs) have been shown to be among the most promising methods because of their ability to model highly complex models at relatively low computational costs. A least-square minimization technique may be used to determine the coefficients of the sparse PCE by relying on the so-called experimental design (ED), i.e., the sample points where the original computational model is evaluated. An efficient sampling strategy is then needed to generate an accurate PCE at low computational cost. This paper is concerned with the problem of identifying an optimal ED that maximizes the accuracy of the surrogate model over the whole input space within a given computational budget. A novel sequential adaptive strategy where the ED is enriched sequentially by capitalizing on the sparsity of the underlying metamodel is introduced. A comparative study between several state-of-the... |
| Databáze: | OpenAIRE |
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