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Books on the topic 'Reliable quantification of uncertainty'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Soize, Christian. Uncertainty Quantification. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0.

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2

Sullivan, T. J. Introduction to Uncertainty Quantification. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23395-6.

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3

Ghanem, Roger, David Higdon, and Houman Owhadi, eds. Handbook of Uncertainty Quantification. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-11259-6.

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4

Dienstfrey, Andrew M., and Ronald F. Boisvert, eds. Uncertainty Quantification in Scientific Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32677-6.

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5

Le Maître, O. P., and Omar M. Knio. Spectral Methods for Uncertainty Quantification. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3520-2.

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6

Grigoriu, Mircea. Stochastic Systems: Uncertainty Quantification and Propagation. London: Springer London, 2012.

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7

McClarren, Ryan G. Uncertainty Quantification and Predictive Computational Science. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0.

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8

Bijl, Hester, Didier Lucor, Siddhartha Mishra, and Christoph Schwab, eds. Uncertainty Quantification in Computational Fluid Dynamics. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00885-1.

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9

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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10

Atamturktur, H. Sezer, Babak Moaveni, Costas Papadimitriou, and 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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11

Jin, Shi, and Lorenzo Pareschi, eds. Uncertainty Quantification for Hyperbolic and Kinetic Equations. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67110-9.

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12

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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13

Barthorpe, Robert, Roland Platz, Israel Lopez, Babak Moaveni, and 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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14

Atamturktur, Sez, Tyler Schoenherr, Babak Moaveni, and 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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15

Atamturktur, H. Sezer, Babak Moaveni, Costas Papadimitriou, and 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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16

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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17

D'Elia, Marta, Max Gunzburger, and Gianluigi Rozza, eds. Quantification of Uncertainty: Improving Efficiency and Technology. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48721-8.

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18

Large-scale inverse problems and quantification of uncertainty. Hoboken, N.J: Wiley, 2010.

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19

Simmermacher, Todd, Scott Cogan, Babak Moaveni, and 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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20

Papadrakakis, Manolis, and George Stefanou, eds. Multiscale Modeling and Uncertainty Quantification of Materials and Structures. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06331-7.

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21

Simmermacher, T., S. Cogan, L. G. Horta, and 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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22

Montomoli, Francesco, ed. Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92943-9.

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23

Montomoli, Francesco, Mauro Carnevale, Antonio D'Ammaro, Michela Massini, and Simone Salvadori. Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14681-2.

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24

De Cursi, José Eduardo Souza, ed. Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53669-5.

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25

National Research Council (U.S.). Division on Engineering and Physical Sciences, ed. Evaluation of quantification of margins and uncertainties methodology for assessing and certifying the reliability of the nuclear stockpile. Washington, D.C: National Academies Press, 2009.

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26

National Research Council (U.S.). Committee on the Evaluation of Quantification of Margins and Uncertainties Methodology for Assessing and Certifying the Reliability of the Nuclear Stockpile. Evaluation of quantification of margins and uncertainties methodology for assessing and certifying the reliability of the nuclear stockpile. Washington, D.C: National Academies Press, 2009.

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27

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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28

Fluids Engineering Conference (1993 Washington, D.C.). Quantification of uncertainty in computational fluid dynamics: Presented at the Fluids Engineering Conference, Washington, D.C., June 20-24, 1993. New York, N.Y: ASME, United Engineering Center, 1993.

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29

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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30

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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31

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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Abstract:

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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32

Sullivan, T. J. Introduction to Uncertainty Quantification. Springer, 2019.

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33

Higdon, David, Houman Owhadi, and Roger Ghanem. Handbook of Uncertainty Quantification. Springer, 2017.

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34

Sullivan, T. J. Introduction to Uncertainty Quantification. Springer, 2015.

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35

Sarkar, Sunetra, and Jeroen A. S. Witteveen. Uncertainty Quantification in Computational Science. World Scientific Publishing Co Pte Ltd, 2016.

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36

Uncertainty Quantification Techniques in Statistics. MDPI, 2020. http://dx.doi.org/10.3390/books978-3-03928-547-1.

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37

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

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38

Sarkar, Sunetra, and Jeroen A. S. Witteveen. Uncertainty Quantification in Computational Science. WORLD SCIENTIFIC, 2015. http://dx.doi.org/10.1142/9854.

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39

Grigoriu, Mircea. Stochastic Systems: Uncertainty Quantification and Propagation. Springer, 2014.

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40

Schwab, Christoph, Hester Bijl, Didier Lucor, and Siddhartha Mishra. Uncertainty Quantification in Computational Fluid Dynamics. Springer, 2013.

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41

McDowell, David L., and Yan Wang. Uncertainty Quantification in Multiscale Materials Modeling. Elsevier Science & Technology, 2020.

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42

Uncertainty Quantification: Theory, Implementation, and Applications. SIAM-Society for Industrial and Applied Mathematics, 2013.

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43

McDowell, David L., and Yan Wang. Uncertainty Quantification in Multiscale Materials Modeling. Elsevier Science & Technology, 2020.

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44

Uncertainty Quantification in Multiscale Materials Modeling. Elsevier, 2020. http://dx.doi.org/10.1016/c2018-0-00364-3.

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45

Stochastic Systems Uncertainty Quantification And Propagation. Springer, 2012.

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46

Uncertainty Quantification: Advances in Research and Applications. Nova Science Publishers, Incorporated, 2019.

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47

Jin, Shi, and Lorenzo Pareschi. Uncertainty Quantification for Hyperbolic and Kinetic Equations. Springer, 2018.

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48

Cursi, Eduardo Souza de, and Rubens Sampaio. Uncertainty Quantification and Stochastic Modeling with Matlab. Elsevier Science & Technology Books, 2015.

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49

Uncertainty Quantification and Stochastic Modeling with Matlab. Elsevier, 2015. http://dx.doi.org/10.1016/c2014-0-04713-2.

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50

Jin, Shi, and Lorenzo Pareschi. Uncertainty Quantification for Hyperbolic and Kinetic Equations. Springer, 2018.

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