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--- glossary [2019/03/08 11:26]
a.nikishova@uva.nl
+++ glossary [2019/03/15 10:21]
dave.wright@ucl.ac.uk Edit in line with Derek's concern about mapping ABM to conceptual rather than mathematical models.
@@ Line 5: / Line 5: @@
 **Validation** is the process of analyzing the accuracy with which the model represents the real world process  (Oberkampf, 2010).
-**Verification** is the process of identifying whether the computational model accurately simulates the mathematical model and its solution (Oberkampf, 2010). Verification can be divided into code verification (finding and fixing errors in the numerical algorithms or in the source code, ensuring good programming practices) and solution verification (estimation of the numerical error).
+**Verification** is the process of identifying whether the computational model accurately simulates the underlying (usually mathematical) model and its solution (Oberkampf, 2010). Verification can be divided into code verification (finding and fixing errors in the numerical algorithms or in the source code, ensuring good programming practices) and solution verification (estimation of the numerical error).
 **Uncertainty Quantification (UQ)** is the discipline, which seeks to estimate uncertainty in the model input and output parameters, to analyse the sources of these uncertainties, and to reduce their quantities.
@@ Line 11: / Line 11: @@
 ====== 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]].
@@ Line 99: / Line 99: @@
 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.
+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
-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.
+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
-Saltelli, Andrea, and Paola Annoni. "How to avoid a perfunctory sensitivity analysis." Environmental Modelling & Software 25.12 (2010): 1508-1517.
+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
-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.
+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
-Soize, Christian. Uncertainty Quantification. Springer International Publishing AG, 2017.
+Soize, Christian. Uncertainty Quantification. Springer International Publishing AG, 2017. https://link.springer.com/book/10.1007/978-3-319-54339-0
-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.
+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
-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.
+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

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