Validation Metrics for Deterministic and Probabilistic Data

[+] Author and Article Information
Kathryn Maupin

Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM 87185-0101

Laura Swiler

Sandia National Laboratories, P.O. Box 5800, Albuquerque, NM 87185-0101

Nathan Porter

North Carolina State University, 3140 Burlington Engineering Labs, 2500 Stinson Drive, Raleigh, NC 27695

1Corresponding author.

ASME doi:10.1115/1.4042443 History: Received June 21, 2018; Revised November 02, 2018


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 to use, as well as how to use and decipher the results. An example is included that compares interpretations of various metrics and demonstrates the impact of model and experimental uncertainty on validation processes.

Sandia National Laboratories (SNL)
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