Probability of failure sensitivity with respect to decision variables
| Autor: | Sylvain Lacaze, Mathieu Balesdent, Samy Missoum, Loïc Brevault |
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| Přispěvatelé: | University of Arizona, ONERA - The French Aerospace Lab [Palaiseau], ONERA-Université Paris Saclay (COmUE) |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Mathematical optimization
Control and Optimization Monte Carlo method Dirac (software) [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS] Estimator Computer Graphics and Computer-Aided Design Computer Science Applications PROBABILITY Distribution (mathematics) Indicator function Control and Systems Engineering SUBSET SAMPLING Failure domain Subset simulation Marginal distribution SENSITIVITY Software Mathematics |
| Zdroj: | Structural and Multidisciplinary Optimization Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2015, 52, p. 375-381. ⟨10.1007/s00158-015-1232-1⟩ |
| ISSN: | 1615-147X 1615-1488 |
| DOI: | 10.1007/s00158-015-1232-1⟩ |
| Popis: | International audience; This note introduces a derivation of the sensitivities of a probability of failure with respect to decision variables. For instance, the gradient of the probability of failure with respect to deterministic design variables might be needed in RBDO. These sensitivities might also be useful for Uncertainty-based Multidisciplinary Design Optimization. The difficulty stems from the dependence of the failure domain on variations of the decision variables. This dependence leads to a derivative of the indicator function in the form of a Dirac distribution in the expression of the sensitivities. Based on an approximation of the Dirac, an estimator of the sensitivities is analytically derived in the case of Crude Monte Carlo first and Subset Simulation. The choice of the Dirac approximation is discussed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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