glossary
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| ====== VECMA Specific Terms ====== | ====== VECMA Specific Terms ====== | ||
| - | **Patterns** within VECMA are abstractions that describe, in a non-application specific manner, a workflow or algorithm for conducting validation, verification, uncertainty quantification or sensitivity analysis. | + | **Patterns** within VECMA are abstractions that describe, in a non-application and non-domain specific manner, a workflow or algorithm for conducting validation, verification, uncertainty quantification or sensitivity analysis. |
| + | The set of patterns described in the proposal is detailed [[proposal_patterns|here]]. | ||
| **Uncertainty Quantification Pattern (UQP)** is the term for workflows and algorithms focussed on uncertainty quantification and propagation or sensitivity analysis. | **Uncertainty Quantification Pattern (UQP)** is the term for workflows and algorithms focussed on uncertainty quantification and propagation or sensitivity analysis. | ||
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| **Sensitivity Analysis** identifies the model portions of uncertainty coming from each uncertain model parameters or group of parameters (Saltelli, 2001). | **Sensitivity Analysis** identifies the model portions of uncertainty coming from each uncertain model parameters or group of parameters (Saltelli, 2001). | ||
| - | **Global sensitivity** methods analyse a model’s sensitivity across the whole variation range of the input parameters (Sobol, 2001). | + | **Global sensitivity** analyse the sensitivity of a model's output to global variations of its inputs, i.e. across the whole variation range of the input parameters (Sobol, 2001). |
| - | **Local sensitivity** methods analyse a particular solution of the model output (Saltelli, 2010). | + | **Local sensitivity** methods analyse the sensitivity of a model's output to local variations of its inputs, i.e. in the neighbourhood of a particular input vecto (Saltelli, 2010). |
| **Surrogate models** or **metamodels** are an alternatives to the original code, which produce approximately similar output in a shorter period of time (Owen, 2017). | **Surrogate models** or **metamodels** are an alternatives to the original code, which produce approximately similar output in a shorter period of time (Owen, 2017). | ||
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| ====== References ====== | ====== References ====== | ||
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| + | B. Chopard, J. Borgdorff and A. G. Hoekstra. “A framework for multi-scale modelling.”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372 (2014), doi: 10.1098/rsta.2013.0378 | ||
| A. Der Kiureghian and O. Ditlevsen, "Aleatory or epistemic? Does it matter?." Structural Safety 31.2 (2009): 105-112, https://doi.org/10.1016/j.strusafe.2008.06.020 | A. Der Kiureghian and O. Ditlevsen, "Aleatory or epistemic? Does it matter?." Structural Safety 31.2 (2009): 105-112, https://doi.org/10.1016/j.strusafe.2008.06.020 | ||
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| R. H. Johnstone, //et al.//, "Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models?." Journal of molecular and cellular cardiology 96 (2016): 49-62, https://doi.org/10.1016/j.yjmcc.2015.11.018 | R. H. Johnstone, //et al.//, "Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models?." Journal of molecular and cellular cardiology 96 (2016): 49-62, https://doi.org/10.1016/j.yjmcc.2015.11.018 | ||
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| J. B. Nagel, "Bayesian techniques for inverse uncertainty quantification". Diss. ETH Zurich, (2017), https://doi.org/10.3929/ethz-a-010835772 | J. B. Nagel, "Bayesian techniques for inverse uncertainty quantification". Diss. ETH Zurich, (2017), https://doi.org/10.3929/ethz-a-010835772 | ||
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| N. E. Owen, //et al.//, "Comparison of surrogate-based uncertainty quantification methods for computationally expensive simulators." SIAM/ASA Journal on Uncertainty Quantification 5.1 (2017): 403-435, https://epubs.siam.org/doi/10.1137/15M1046812 | N. E. Owen, //et al.//, "Comparison of surrogate-based uncertainty quantification methods for computationally expensive simulators." SIAM/ASA Journal on Uncertainty Quantification 5.1 (2017): 403-435, https://epubs.siam.org/doi/10.1137/15M1046812 | ||
| + | Rothschild, Michael, and Joseph E. Stiglitz. "Increasing risk: I. A definition." Journal of Economic theory 2.3 (1970): 225-243. https://doi.org/10.1016/0022-0531(70)90038-4 | ||
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| + | Saltelli, Andrea, et al. "Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index." Computer Physics Communications 181.2 (2010): 259-270. https://doi.org/10.1016/j.cpc.2009.09.018 | ||
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| + | Saltelli, Andrea, and Paola Annoni. "How to avoid a perfunctory sensitivity analysis." Environmental Modelling & Software 25.12 (2010): 1508-1517. https://doi.org/10.1016/j.envsoft.2010.04.012 | ||
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| + | Sobol, Ilya M. "Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates." Mathematics and computers in simulation 55.1-3 (2001): 271-280. https://doi.org/10.1016/S0378-4754(00)00270-6 | ||
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| + | Soize, Christian. Uncertainty Quantification. Springer International Publishing AG, 2017. https://link.springer.com/book/10.1007/978-3-319-54339-0 | ||
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| + | van den Bos, L. M. M., Barry Koren, and Richard P. Dwight. "Non-intrusive uncertainty quantification using reduced cubature rules." Journal of Computational Physics 332 (2017): 418-445. https://doi.org/10.1016/j.jcp.2016.12.011 | ||
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| + | Wu, Xu, and Tomasz Kozlowski. "Inverse uncertainty quantification of reactor simulations under the Bayesian framework using surrogate models constructed by polynomial chaos expansion." Nuclear Engineering and Design 313 (2017): 29-52. https://doi.org/10.1016/j.nucengdes.2016.11.032 | ||
glossary.txt · Last modified: 2019/04/03 10:26 by dave.wright@ucl.ac.uk
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