Quantification of Uncertainty: Improving Efficiency and Technology: QUIET selected contributions (Lecture Notes in Computational Science and Engineering (137), Band 137)
This book explores four guiding themes – reduced order modelling, high dimensional problems, efficient algorithms, and applications – by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book’s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.
| yazar | Springer; 1st ed. 2020 basım |
|---|---|
| Boyutlar ve boyutlar | 15.6 x 1.75 x 23.39 cm |
| Tarafından yayınlandı | 25 Eylül 2020 |
Additional Contributors 18,9 x 0,6 x 24,6 cm ROBERT H BORK 3 Ocak 2017 18,9 x 0,5 x 24,6 cm 30 Ekim 2011 Mdpi AG Kolektif 18,9 x 0,2 x 24,6 cm 25 Eylül 2020 18,9 x 0,4 x 24,6 cm 1 Ocak 2017 WADE H MCCREE 15 x 0,5 x 22 cm 28 Ekim 2011 ERWIN N GRISWOLD 18,9 x 0,3 x 24,6 cm 28 Şubat 2018
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Sürüm ayrıntıları
| yazar | Springer; 1st ed. 2020 basım |
|---|---|
| isbn 13 | 978-3030487201 |
| Yayımcı | Springer; 1st ed. 2020 basım |
| Boyutlar ve boyutlar | 15.6 x 1.75 x 23.39 cm |
| Tarafından yayınlandı Quantification of Uncertainty: Improving Efficiency and Technology: QUIET selected contributions (Lecture Notes in Computational Science and Engineering (137), Band 137) | 25 Eylül 2020 |
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