On the fundamental tautology of validating data-driven models and simulations

Authors:
John Michopoulos;Sam Lambrakos
Affiliations:
Materials Science and Technology Division, U.S. Naval Research Laboratory, Washington, DC;Materials Science and Technology Division, U.S. Naval Research Laboratory, Washington, DC
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Year:
2005

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Abstract

Recent advances in Dynamic Data Driven Application Systems (DDDAS) facilitated by the present level of computational technologies, as well as advances in data-driven modeling and simulation, impose the need for a critical evaluation of paradigms underlying Qualification, Validation and Verification (QV&V). This paper discusses the fundamental irrelevance of conventional validation procedures with respect to data-driven models and simulations. This inherent property of data-driven models and simulations makes the data-driven approaches extremely desirable from a reliability perspective. An informal comparison of the logical flow of traditional and evolved QV&V demonstrates the tautological nature of data-driven model validation. A brief epistemological review of the origins of traditional and evolved QV&V is also presented.