Validation Metrics for Deterministic and Probabilistic Data
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Computational modeling and simulation are paramount to modern science. Computational models often replace physical experiments that are prohibitively expensive, dangerous, or occur at extreme scales. Thus, it is critical that these models accurately represent and can be used as replacements for reality. This paper provides an analysis of metrics that may be used to determine the validity of a computational model. While some metrics have a direct physical meaning and a long history of use, others, especially those that compare probabilistic data, are more difficult to interpret. Furthermore, the process of model validation is often application-specific, making the procedure itself challenging and the results difficult to defend. We therefore provide guidance and recommendations as to which validation metric to use, as well as how to use and decipher the results. Furthermore an example is included that compares interpretations of various metrics and demonstrates the impact of model and experimental uncertainty on validation processes.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1496637
- Report Number(s):
- SAND--2019-0812J; 671836
- Journal Information:
- Journal of Verification, Validation and Uncertainty Quantification, Journal Name: Journal of Verification, Validation and Uncertainty Quantification Journal Issue: 3 Vol. 3; ISSN 2377-2158
- Publisher:
- ASMECopyright Statement
- Country of Publication:
- United States
- Language:
- English
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