| 书目名称 | Uncertainty Quantification and Predictive Computational Science | | 副标题 | A Foundation for Phy | | 编辑 | Ryan G. McClarren | | 视频video | | | 概述 | Organizes wide-ranging and interdisciplinary topics of uncertainty quantification from multiple sources into a single teaching text.Reviews the fundamentals of probability and statistics.Guides the tr | | 图书封面 |  | | 描述 | .This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences...Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions underuncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. | | 出版日期 | Textbook 2018 | | 关键词 | parametric uncertainty quantification; sensitivity analysis; Reliability Methods; Monte Carlo Methods; P | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-99525-0 | | isbn_ebook | 978-3-319-99525-0 | | copyright | Springer Nature Switzerland AG 2018 |
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发表于 2025-3-21 16:29:01
