| 書目名稱 | Uncertainty Quantification |
| 副標(biāo)題 | An Accelerated Cours |
| 編輯 | Christian Soize |
| 視頻video | http://file.papertrans.cn/942/941098/941098.mp4 |
| 概述 | Presents fundamental mathematical tools of probability and statistics that are directly useful for uncertainty quantification.Includes several topics not currently published in research monographs.Cov |
| 叢書名稱 | Interdisciplinary Applied Mathematics |
| 圖書封面 |  |
| 描述 | This book presents the fundamental notions and advanced mathematical tools in the stochastic modeling of uncertainties and their quantification for large-scale computational models in sciences and engineering. In particular, it focuses in parametric uncertainties, and non-parametric uncertainties with applications from the structural dynamics and vibroacoustics of complex mechanical systems, from micromechanics and multiscale mechanics of heterogeneous materials.?.Resulting from a course developed by the author, the book begins with a description of the fundamental mathematical tools of probability and statistics that are directly useful for uncertainty quantification. It proceeds with a well carried out description of some basic and advanced methods for constructing stochastic models of uncertainties, paying particular attention to the problem of calibrating and identifying a stochastic model of uncertainty when experimental data is available.?.This book is intended to be a graduate-level textbook for students as well as professionals interested in the theory, computation, and applications of risk and prediction in science and engineering fields.. |
| 出版日期 | Textbook 2017 |
| 關(guān)鍵詞 | High Stochastic Dimension; Maximum Entropy Principle; MCMC Methods; Model Uncertainties; Model-parameter |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-54339-0 |
| isbn_softcover | 978-3-319-85372-7 |
| isbn_ebook | 978-3-319-54339-0Series ISSN 0939-6047 Series E-ISSN 2196-9973 |
| issn_series | 0939-6047 |
| copyright | Springer International Publishing AG 2017 |
|Archiver|手機版|小黑屋|
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