Abstract
The validation of numerical models often progresses incrementally from previous models or other numerical solutions or is undertaken by comparison with experimentally obtained reference measurements. Notwithstanding the accuracy of the reference results, quantification of the error between the two is important information in deciding the quality of the model. It is frequently the case that this estimate of error is done by eye. However, for purposes of traceability and objectivity, interest has started to focus on techniques to quantify this error in an algorithmic manner in a way that agrees with the general observations of experienced engineers. This paper reviews two of the most promising techniques, namely Feature Selective Validation (FSV) and Integrated Error against Logarithmic Frequency (IELF), putting them in the context of correlation and reliability functions.
| Original language | English |
|---|---|
| Pages | 11-16 |
| Number of pages | 6 |
| Volume | 19 |
| No. | 1 |
| Specialist publication | Applied Computational Electromagnetics Society Newsletter |
| Publication status | Published - Mar 2004 |
| Externally published | Yes |
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Keywords
- Algorithms
- Correlation methods
- Fourier transforms
- Graph theory
- Mathematical models
- Probability density function
- Reliability
ASJC Scopus subject areas
- Media Technology
- Physics and Astronomy(all)
Cite this
Validating numerical models - Quantifying error. / Duffy, Alistair; Coleby, Dawn; Martin, Anthony; Woolfson, Malcolm; Benson, Trevor.
In: Applied Computational Electromagnetics Society Newsletter, Vol. 19, No. 1, 03.2004, p. 11-16.Research output: Contribution to specialist publication › Article
}
TY - GEN
T1 - Validating numerical models - Quantifying error
AU - Duffy, Alistair
AU - Coleby, Dawn
AU - Martin, Anthony
AU - Woolfson, Malcolm
AU - Benson, Trevor
PY - 2004/3
Y1 - 2004/3
N2 - The validation of numerical models often progresses incrementally from previous models or other numerical solutions or is undertaken by comparison with experimentally obtained reference measurements. Notwithstanding the accuracy of the reference results, quantification of the error between the two is important information in deciding the quality of the model. It is frequently the case that this estimate of error is done by eye. However, for purposes of traceability and objectivity, interest has started to focus on techniques to quantify this error in an algorithmic manner in a way that agrees with the general observations of experienced engineers. This paper reviews two of the most promising techniques, namely Feature Selective Validation (FSV) and Integrated Error against Logarithmic Frequency (IELF), putting them in the context of correlation and reliability functions.
AB - The validation of numerical models often progresses incrementally from previous models or other numerical solutions or is undertaken by comparison with experimentally obtained reference measurements. Notwithstanding the accuracy of the reference results, quantification of the error between the two is important information in deciding the quality of the model. It is frequently the case that this estimate of error is done by eye. However, for purposes of traceability and objectivity, interest has started to focus on techniques to quantify this error in an algorithmic manner in a way that agrees with the general observations of experienced engineers. This paper reviews two of the most promising techniques, namely Feature Selective Validation (FSV) and Integrated Error against Logarithmic Frequency (IELF), putting them in the context of correlation and reliability functions.
KW - Algorithms
KW - Correlation methods
KW - Fourier transforms
KW - Graph theory
KW - Mathematical models
KW - Probability density function
KW - Reliability
M3 - Article
VL - 19
SP - 11
EP - 16
JO - Applied Computational Electromagnetics Society Newsletter
JF - Applied Computational Electromagnetics Society Newsletter
SN - 1056-9170
ER -