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

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. © Springer-Verlag Berlin Heidelberg 2005.