Adaptive sparse polynomial dimensional decomposition for derivative-based sensitivity
| Autor: | Jonathan M. Wang, Kunkun Tang, Jonathan B. Freund |
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| Rok vydání: | 2019 |
| Předmět: |
Numerical Analysis
Physics and Astronomy (miscellaneous) Computer science Applied Mathematics Monte Carlo method Sobol sequence 010103 numerical & computational mathematics Function (mathematics) Derivative 01 natural sciences Computer Science Applications 010101 applied mathematics Computational Mathematics Modeling and Simulation Orthogonal polynomials Decomposition (computer science) Probability distribution Sensitivity (control systems) 0101 mathematics Algorithm |
| Zdroj: | Journal of Computational Physics. 391:303-321 |
| ISSN: | 0021-9991 |
| DOI: | 10.1016/j.jcp.2019.04.042 |
| Popis: | For applications, such as the plasma-coupled combustion system we consider, derivative-based sensitivity indices (DSI) are known to have several advantages over Sobol's total sensitivity indices, especially for small sample sizes. Several properties of derivative-based sensitivity measures are leveraged to develop a new and efficient numerical approach to estimate them. It is based on computing the DSI measures by effectively cost-free Monte Carlo sampling of an adaptively constructed orthogonal polynomial surrogate with uncertain input parameters that can have arbitrary probability distributions. The adaptivity reduces the number of necessary model evaluations, which is demonstrated both in a constructed example (the Moon function) and in two plasma-combustion systems with up to 55 uncertain parameters. Unimportant parameters are successfully identified and neglected with a low number of model evaluations, which makes it an attractive non-intrusive approach when adjoint solutions are unavailable to provide sensitivity information. |
| Databáze: | OpenAIRE |
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