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710	2		_aSpringerLink (Online service) _9106996
999			_c110918 _d110918
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020			_a9783662583951
024	7		_a10.1007/978-3-662-58395-1 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQA279.2 _b2019 EB
100	1		_aCacuci, Dan Gabriel _eautor _9670861
245	1	0	_aBERRU Predictive Modeling : _bbest estimate results with reduced uncertainties _cby Dan Gabriel Cacuci
264		1	_aBerlin, Heidelberg _bSpringer Berlin Heidelberg : _bImprint: Springer _c2019
300			_a1 recurso en línea (XIV, 451 páginas) _b1 ilustración
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
505	0		_aBasics 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	3		_aThis 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		7	_2embne _9670862 _aPredicción, Teoría de la
650		7	_2embne _aEstadística _9138934
776	0	8	_iPrinted edition: _z9783662583937
776	0	8	_iPrinted edition: _z9783662583944
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-662-58395-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Engineering
998			_aSI _cm _dz _feng _ggw _h0 _b09/2019 _eel _zSI