| 期刊全称 | BERRU Predictive Modeling | | 期刊简称 | Best Estimate Result | | 影响因子2023 | Dan Gabriel Cacuci | | 视频video | | | 发行地址 | The first-ever book on the experimental calibration of best estimate numerical simulation models.Demonstrates the model’s implementation using various examples, e.g. from chemistry, geophysics, space | | 图书封面 |  | | 影响因子 | .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 author’s 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. .The customary methods used for data assimilation combine experimental and computational information by minimizing an a priori, user-chosen, “cost functional” (usually a quadratic functional that represents the weighted errors between measured and computed responses). In contrast to these user-influenced methods, the BERRU (Best Estimate Results with Reduced Uncertainties) Predictive Model | | Pindex | Book 2019 |
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发表于 2025-3-21 18:50:44
