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https://doi.org/10.1007/978-3-030-04870-9Uncertainty Quantification; Surrogate Models; Response Surfaces; Stochastic Models; Sensitivity AnalysisGrievance 发表于 2025-3-22 02:55:03
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Flavio CanaveroShares cutting edge uncertainty quantification ideas with a wide audience.Draws together the work of engineers, mathematicians, physicists, and statisticians.Provides a view of a traditionally mathemaaviator 发表于 2025-3-22 16:43:05
A New Approach for State Estimation, the state: moment matching (MM), collocation (COL) and variational (VAR). We show that the method is effective to calculate by using two significant examples: a classical discrete linear system containing difficulties and the Influenza in a boarding school. In all these examples, the proposed appro震惊 发表于 2025-3-22 17:37:54
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Hybrid Possibilistic-Probabilistic Approach to Uncertainty Quantification in Electromagnetic Compatilistic nature of the two sets of involved parameters. Two methods to aggregate the obtained random-fuzzy sets are presented and compared versus the results obtained by running fully-probabilistic Monte Carlo (MC) simulations, where all uncertain parameters were assigned known probability distributi