Social Sciences → Decision Sciences → Statistics, Probability and Uncertainty
Probabilistic and Robust Engineering Design
This cluster of papers focuses on uncertainty quantification and sensitivity analysis in complex mathematical and computational models. It explores methods such as polynomial chaos, Monte Carlo simulation, and sparse grids to assess and manage uncertainties in various engineering and scientific applications. The research also delves into topics like global sensitivity indices, reliability analysis, stochastic differential equations, and probabilistic design optimization.
75.7K works worldwide1.2M citations
Uncertainty QuantificationSensitivity AnalysisPolynomial ChaosMonte Carlo SimulationReliability AnalysisStochastic Differential EquationsGlobal Sensitivity IndicesSparse GridsLatin Hypercube SamplingProbabilistic Design Optimization
Journals publishing in this area
2
International Journal for Numerical Methods in Engineering
ISSN 0029-59811,321 articles in this topic
270h-index
2.32Impact
15.4KArticles
568.2KCitations
15
Journal of the Royal Statistical Society Series B (Statistical Methodology)
ISSN 1369-7412120 articles in this topic
307h-index
1.61Impact
4.2KArticles
739.3KCitations

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