glossary
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glossary [2019/03/15 09:33] dave.wright@ucl.ac.uk |
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| **Validation** is the process of analyzing the accuracy with which the model represents the real world process (Oberkampf, 2010). | **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. | **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. | ||
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| **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). | **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 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). | + | **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 vector (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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