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Libros sobre el tema "Uncertainty Quantification model"

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Publicado: 31 de agosto de 2024

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1

Mao, Zhu, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-77348-9.

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Mao, Zhu, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-04090-0.

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Barthorpe, Robert, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-74793-4.

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4

Atamturktur, H. Sezer, Babak Moaveni, Costas Papadimitriou y Tyler Schoenherr, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04552-8.

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Barthorpe, Robert, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-12075-7.

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6

Atamturktur, Sez, Tyler Schoenherr, Babak Moaveni y Costas Papadimitriou, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5.

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7

Barthorpe, Robert, Roland Platz, Israel Lopez, Babak Moaveni y Costas Papadimitriou, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54858-6.

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8

Mao, Zhu, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0.

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9

Atamturktur, H. Sezer, Babak Moaveni, Costas Papadimitriou y Tyler Schoenherr, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0.

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10

Platz, Roland, Garrison Flynn, Kyle Neal y Scott Ouellette, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-37003-8.

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11

Simmermacher, Todd, Scott Cogan, Babak Moaveni y Costas Papadimitriou, eds. Topics in Model Validation and Uncertainty Quantification, Volume 5. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6564-5.

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12

Simmermacher, T., S. Cogan, L. G. Horta y R. Barthorpe, eds. Topics in Model Validation and Uncertainty Quantification, Volume 4. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2431-4.

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13

Workshop on Model Uncertainty, Its Characterization and Quantification (1993 Annapolis, Maryland). Proceedings of Workshop on Model Uncertainty, Its Characterization and Quantification: Annapolis, Maryland, USA, October 20-22, 1993. College Park, MD, U.S.A: University Printing Services, University of Maryland, 1995.

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14

Dong, Chang Bub, Hanʾguk Wŏnjaryŏk Yŏnʾguso, Hanʾguk Wŏnjaryŏk Anjŏn Kisurwŏn y U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research, eds. International agreement report: Improvements to the RELAP5/MOD3 reflood model and uncertainty quantification of reflood peak clad temperature. Washington, D.C: Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1996.

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15

Simmermacher, Todd. Topics in Model Validation and Uncertainty Quantification, Volume 5: Proceedings of the 31st IMAC, A Conference on Structural Dynamics, 2013. New York, NY: Springer New York, 2013.

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16

Simmermacher, T. Topics in Model Validation and Uncertainty Quantification, Volume 4: Proceedings of the 30th IMAC, A Conference on Structural Dynamics, 2012. New York, NY: Springer New York, 2012.

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17

Barthorpe, Robert. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics 2017. Cham: Springer International Publishing, 2017.

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18

A, Mosleh, U.S. Nuclear Regulatory Commission, EG & G Idaho, University of Maryland at College Park y U.S. Nuclear Regulatory Commission. Division of Safety Issue Resolution, eds. Proceedings of Workshop I in Advanced Topics in Risk and Reliability Analysis: Model uncertainty, its characterization and quantification : held at Governor Calvert House and Conference Center, Annapolis, Maryland, October 20-22, 1993. Washington, DC: Division of Safety Issue Resolution, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1994.

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19

Hessling, Jan Peter, ed. Uncertainty Quantification and Model Calibration. InTech, 2017. http://dx.doi.org/10.5772/65579.

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20

Sanderson, Benjamin Mark. Uncertainty Quantification in Multi-Model Ensembles. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.707.

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Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.
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21

Cogan, S., T. Simmermacher, R. Barthorpe y L. G. Horta. Topics in Model Validation and Uncertainty Quantification. Springer, 2012.

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22

Adhikari, Sondipon, Sudip Dey y Tanmoy Mukhopadhyay. Uncertainty Quantification in Laminated Composites: A Meta-Model Based Approach. Taylor & Francis Group, 2018.

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23

Adhikari, Sondipon, Sudip Dey y Tanmoy Mukhopadhyay. Uncertainty Quantification in Laminated Composites: A Meta-Model Based Approach. Taylor & Francis Group, 2018.

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24

Adhikari, Sondipon, Sudip Dey y Tanmoy Mukhopadhyay. Uncertainty Quantification in Laminated Composites: A Meta-Model Based Approach. Taylor & Francis Group, 2018.

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25

Adhikari, Sondipon, Sudip Dey y Tanmoy Mukhopadhyay. Uncertainty Quantification in Laminated Composites: A Meta-Model Based Approach. Taylor & Francis Group, 2018.

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26

Uncertainty Quantification in Laminated Composites: A Meta-Model Based Approach. Taylor & Francis Group, 2018.

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27

Anderson, Mark, Francois Hemez y Scott Doebling. Model Verification and Validation in Engineering Mechanics: Theory and Applications of Uncertainty Quantification and Predictive Accuracy. John Wiley & Sons, 2005.

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28

Model Verification and Validation in Engineering Mechanics: Theory and Applications of Uncertainty Quantification and Predictive Accuracy. Wiley & Sons, Limited, John, 2004.

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29

Moaveni, Babak, Costas Papadimitriou, Tyler Schoenherr y Sez Atamturktur. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 34th IMac, a Conference and Exposition on Structural Dynamics 2016. Springer International Publishing AG, 2016.

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30

Simmermacher, Todd, Scott Cogan y Babak Moaveni. Topics in Model Validation and Uncertainty Quantification, Volume 5: Proceedings of the 31st IMAC, A Conference on Structural Dynamics, 2013. Springer, 2013.

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31

Moaveni, Babak, H. Sezer Atamturktur, Costas Papadimitriou y Tyler Schoenherr. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 32nd IMAC, a Conference and Exposition on Structural Dynamics 2014. Springer London, Limited, 2014.

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32

Mao, Zhu. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 39th IMAC, a Conference and Exposition on Structural Dynamics 2021. Springer International Publishing AG, 2021.

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33

Moaveni, Babak, H. Sezer Atamturktur, Costas Papadimitriou y Tyler Schoenherr. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 33rd IMAC, a Conference and Exposition on Structural Dynamics 2015. Springer, 2015.

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34

Mao, Zhu. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 40th IMAC, a Conference and Exposition on Structural Dynamics 2022. Springer International Publishing AG, 2022.

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35

Platz, Roland, Babak Moaveni, Costas Papadimitriou, Robert Barthorpe y Israel Lopez. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 35th IMAC, a Conference and Exposition on Structural Dynamics 2017. Springer, 2017.

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36

Moaveni, Babak, Costas Papadimitriou, Tyler Schoenherr y Sez Atamturktur. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 34th IMAC, a Conference and Exposition on Structural Dynamics 2016. Springer London, Limited, 2016.

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37

Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 40th IMAC, a Conference and Exposition on Structural Dynamics 2022. Springer International Publishing AG, 2023.

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38

Stevens, Garrison, Kyle Neal y Scott Ouellette. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 41st IMAC, a Conference and Exposition on Structural Dynamics 2023. Springer, 2023.

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39

Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 39th IMAC, a Conference and Exposition on Structural Dynamics 2021. Springer International Publishing AG, 2023.

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40

Mao, Zhu. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020 ... Society for Experimental Mechanics Series). Springer, 2021.

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41

Simmermacher, Todd, Scott Cogan, Babak Moaveni y Costas Papadimitriou. Topics in Model Validation and Uncertainty Quantification, Volume 5: Proceedings of the 31st IMAC, A Conference on Structural Dynamics, 2013 ... Society for Experimental Mechanics Series). Springer, 2013.

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42

Barthorpe, Robert. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019 ... Society for Experimental Mechanics Series). Springer, 2020.

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43

Moaveni, Babak, H. Sezer Atamturktur, Costas Papadimitriou y Tyler Schoenherr. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 33rd IMAC, A Conference and Exposition on Structural Dynamics, 2015 ... Society for Experimental Mechanics Series). Springer, 2016.

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44

Platz, Roland, Babak Moaveni, Costas Papadimitriou, Robert Barthorpe y Israel Lopez. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics 2017 ... Society for Experimental Mechanics Series). Springer, 2018.

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45

Moaveni, Babak, H. Sezer Atamturktur, Costas Papadimitriou y Tyler Schoenherr. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 32nd IMAC, A Conference and Exposition on Structural Dynamics, 2014 ... Society for Experimental Mechanics Series). Springer, 2016.

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46

Moaveni, Babak, H. Sezer Atamturktur, Costas Papadimitriou y Tyler Schoenherr. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 32nd IMAC, A Conference and Exposition on Structural Dynamics, 2014 ... Society for Experimental Mechanics Series). Springer, 2014.

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47

Moaveni, Babak, H. Sezer Atamturktur, Costas Papadimitriou y Tyler Schoenherr. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 33rd IMAC, A Conference and Exposition on Structural Dynamics, 2015 ... Society for Experimental Mechanics Series). Springer, 2015.

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48

Barthorpe, Robert. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019 ... Society for Experimental Mechanics Series). Springer, 2019.

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49

Barthorpe, Robert. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 36th IMAC, A Conference and Exposition on Structural Dynamics 2018 ... Society for Experimental Mechanics Series). Springer, 2018.

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50

Moaveni, Babak, Costas Papadimitriou, Tyler Schoenherr y Sez Atamturktur. Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 34th IMAC, A Conference and Exposition on Structural Dynamics 2016 ... Society for Experimental Mechanics Series). Springer, 2018.

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