國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

BERRU predictive modelingbest estima...

Cacuci, Dan Gabriel.

BERRU predictive modelingbest estimate results with reduced uncertainties /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	BERRU predictive modelingby Dan Gabriel Cacuci.
其他題名:	best estimate results with reduced uncertainties /
作者:	Cacuci, Dan Gabriel.
出版者:	Berlin, Heidelberg :Springer Berlin Heidelberg :2019.
面頁冊數:	xiv, 451 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Prediction theory.
電子資源:	https://doi.org/10.1007/978-3-662-58395-1
ISBN:	9783662583951$q(electronic bk.)

BERRU predictive modelingbest estimate results with reduced uncertainties /
Cacuci, Dan Gabriel.

BERRU predictive modelingbest estimate results with reduced uncertainties /[electronic resource] :by Dan Gabriel Cacuci. - Berlin, Heidelberg :Springer Berlin Heidelberg :2019. - xiv, 451 p. :ill., digital ;24 cm.

Basics of predictive best-estimate model calibration -- Predictive best-estimate model-validation, model-calibration and model-verification concerning open and chaotic systems -- Differences to traditional statostic evaluation methods -- Examples.

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 Modeling methodology developed by the author relies on the thermodynamics-based maximum entropy principle to eliminate the need for relying on minimizing user-chosen functionals, thus generalizing the "data adjustment" and/or the "4D-VAR" data assimilation procedures used in the geophysical sciences. The BERRU predictive modeling methodology also provides a "model validation metric" which quantifies the consistency (agreement/disagreement) between measurements and computations. This "model validation metric" (or "consistency indicator") is constructed from parameter covariance matrices, response covariance matrices (measured and computed), and response sensitivities to model parameters. Traditional methods for computing response sensitivities are hampered by the "curse of dimensionality," which makes them impractical for applications to large-scale systems that involve many imprecisely known parameters. Reducing the computational effort required for precisely calculating the response sensitivities is paramount, and the comprehensive adjoint sensitivity analysis methodology developed by the author shows great promise in this regard, as shown in this book. After discarding inconsistent data (if any) using the consistency indicator, the BERRU predictive modeling methodology provides best-estimate values for predicted parameters and responses along with best-estimate reduced uncertainties (i.e., smaller predicted standard deviations) for the predicted quantities. Applying the BERRU methodology yields optimal, experimentally validated, "best estimate" predictive modeling tools for designing new technologies and facilities, while also improving on existing ones.

ISBN: 9783662583951$q(electronic bk.)

Standard No.: 10.1007/978-3-662-58395-1doiSubjects--Topical Terms:

181891
Prediction theory.

LC Class. No.: QA279.2 / .C338 2019

Dewey Class. No.: 519.287

BERRU predictive modelingbest estimate results with reduced uncertainties /
LDR:04122nmm a2200325 a 4500 001 555603
003 DE-He213
005 20190704104754.0
006 m d
007 cr nn 008maaau
008 191121s2019 gw s 0 eng d
020 $a 9783662583951$q(electronic bk.)
020 $a 9783662583937$q(paper)
024 7 $a 10.1007/978-3-662-58395-1 $2 doi
035 $a 978-3-662-58395-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279.2 $b .C338 2019
072 7 $a TBJ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a TBJ $2 thema
082 0 4 $a 519.287 $2 23
090 $a QA279.2 $b .C119 2019
100 1 $a Cacuci, Dan Gabriel. $3 495560
245 1 0 $a BERRU predictive modeling $h [electronic resource] : $b best estimate results with reduced uncertainties / $c by Dan Gabriel Cacuci.
260 $a Berlin, Heidelberg : $b Springer Berlin Heidelberg : $b Imprint: Springer, $c 2019.
300 $a xiv, 451 p. : $b ill., digital ; $c 24 cm.
505 0 $a Basics of predictive best-estimate model calibration -- Predictive best-estimate model-validation, model-calibration and model-verification concerning open and chaotic systems -- Differences to traditional statostic evaluation methods -- Examples.
520 $a 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 Modeling methodology developed by the author relies on the thermodynamics-based maximum entropy principle to eliminate the need for relying on minimizing user-chosen functionals, thus generalizing the "data adjustment" and/or the "4D-VAR" data assimilation procedures used in the geophysical sciences. The BERRU predictive modeling methodology also provides a "model validation metric" which quantifies the consistency (agreement/disagreement) between measurements and computations. This "model validation metric" (or "consistency indicator") is constructed from parameter covariance matrices, response covariance matrices (measured and computed), and response sensitivities to model parameters. Traditional methods for computing response sensitivities are hampered by the "curse of dimensionality," which makes them impractical for applications to large-scale systems that involve many imprecisely known parameters. Reducing the computational effort required for precisely calculating the response sensitivities is paramount, and the comprehensive adjoint sensitivity analysis methodology developed by the author shows great promise in this regard, as shown in this book. After discarding inconsistent data (if any) using the consistency indicator, the BERRU predictive modeling methodology provides best-estimate values for predicted parameters and responses along with best-estimate reduced uncertainties (i.e., smaller predicted standard deviations) for the predicted quantities. Applying the BERRU methodology yields optimal, experimentally validated, "best estimate" predictive modeling tools for designing new technologies and facilities, while also improving on existing ones.
650 0 $a Prediction theory. $3 181891
650 1 4 $a Engineering Mathematics. $3 806481
650 2 4 $a Simulation and Modeling. $3 273719
650 2 4 $a Mathematical Modeling and Industrial Mathematics. $3 274070
650 2 4 $a Quality Control, Reliability, Safety and Risk. $3 274011
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-3-662-58395-1
950 $a Engineering (Springer-11647)