LEADER 04285nam 2200577Ia 450 001 9910785765903321 005 20230801224352.0 010 $a0-309-25637-2 010 $a1-283-63622-0 010 $a0-309-25635-6 035 $a(CKB)2670000000241223 035 $a(EBL)3378995 035 $a(SSID)ssj0000665815 035 $a(PQKBManifestationID)11447237 035 $a(PQKBTitleCode)TC0000665815 035 $a(PQKBWorkID)10646852 035 $a(PQKB)10916736 035 $a(MiAaPQ)EBC3378995 035 $a(Au-PeEL)EBL3378995 035 $a(CaPaEBR)ebr10594220 035 $a(CaONFJC)MIL394868 035 $a(OCoLC)798361535 035 $a(EXLCZ)992670000000241223 100 $a20120405d2012 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAssessing the reliability of complex models$b[electronic resource] $emathematical and statistical foundations of verification, validation, and uncertainty quantification /$fCommittee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification ; Board on Mathematical Sciences and Their Applications ; Division on Engineering and Physical Sciences, National Research Council of the National Academies 210 $aWashington, D.C. $cNational Academies Press$dc2012 215 $a1 online resource (145 p.) 300 $aDescription based upon print version of record. 311 $a0-309-25634-8 320 $aIncludes bibliographical references. 327 $a""Front Matter""; ""Acknowledgments""; ""Contents""; ""Summary""; ""1 Introduction""; ""2 Sources of Uncertainty and Error""; ""3 Verification""; ""4 Emulation, Reduced-Order Modeling, and Forward Propagation""; ""5 Model Validation and Prediction""; ""6 Making Decisions""; ""7 Next Steps in Practice, Research, and Education for Verification, Validation, and Uncertainty Quantification""; ""Appendixes""; ""Appendix A: Glossary""; ""Appendix B: Agendas of Committee Meetings""; ""Appendix C: Committee Biographies""; ""Appendix D: Acronyms"" 330 $a"Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if at all. Assessing the Reliability of Complex Models recognizes the ubiquity of uncertainty in computational estimates of reality and the necessity for its quantification. As computational science and engineering have matured, the process of quantifying or bounding uncertainties in a computational estimate of a physical quality of interest has evolved into a small set of interdependent tasks: verification, validation, and uncertainty of quantification (VVUQ). In recognition of the increasing importance of computational simulation and the increasing need to assess uncertainties in computational results, the National Research Council was asked to study the mathematical foundations of VVUQ and to recommend steps that will ultimately lead to improved processes. Assessing the Reliability of Complex Models discusses changes in education of professionals and dissemination of information that should enhance the ability of future VVUQ practitioners to improve and properly apply VVUQ methodologies to difficult problems, enhance the ability of VVUQ customers to understand VVUQ results and use them to make informed decisions, and enhance the ability of all VVUQ stakeholders to communicate with each other. This report is an essential resource for all decision and policy makers in the field, students, stakeholders, UQ experts, and VVUQ educators and practitioners."--Publisher's description. 606 $aComputer simulation 606 $aUncertainty$xMathematical models 615 0$aComputer simulation. 615 0$aUncertainty$xMathematical models. 676 $a003.3 712 02$aNational Research Council (U.S.) 712 02$aNational Academies Press (U.S.) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910785765903321 996 $aAssessing the reliability of complex models$93726302 997 $aUNINA