BERRU predictive modeling: best estimate results with reduced uncertainties
Dan Gabriel Cacuci
(Springer Nature, 2019)
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| This book addresses the experimental calibration of best-estimate numerical simulation models. The results of measurements and computations are never exact. Therefore, knowing only the nominal values of experimentally measured or computed quantities is insufficient for applications, particularly since the respective experimental and computed nominal values seldom coincide. In the authors view, the objective of predictive modeling is to extract best estimate values for model parameters and predicted results, together with best estimate uncertainties for these parameters and results. To achieve this goal, predictive modeling combines imprecisely known experimental and computational data, which calls for reasoning on the basis of incomplete, error-rich, and occasionally discrepant information. |
 BERRU Predictive Modeling.pdf :: Unduh
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| No. Panggil : | e20507008 |
| Entri utama-Nama orang : | |
| Subjek : | |
| Penerbitan : | Berlin: Springer Nature, 2019 |
| Sumber Pengatalogan: | LibUI eng rda |
| Tipe Konten: | text |
| Tipe Media: | computer |
| Tipe Pembawa: | online resource |
| Deskripsi Fisik: | xiv, 451 pages : illustration |
| Tautan: | https://doi.org/10.1007/978-3-662-58395-1 |
| Lembaga Pemilik: | |
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| No. Panggil | No. Barkod | Ketersediaan |
|---|---|---|
| e20507008 | 02-20-715254254 | TERSEDIA |
| Ulasan: |
| Tidak ada ulasan pada koleksi ini: 20507008 |
