A New Uncertainty Analysis-Based Framework for Data-Driven Computational Mechanics

Abstract

Abstract In this article, a new uncertainty analysis-based framework for data-driven computational mechanics (DDCM) is established. Compared with its practical classical counterpart, the distinctive feature of this framework is that uncertainty analysis is introduced into the corresponding problem formulation explicitly. Instated of only focusing on a single solution in phase space, a solution set is sought for to account for the influence of the multisource uncertainties associated with the data set on the data-driven solutions. An illustrative example provided shows that the proposed framework is not only conceptually new but also has the potential of circumventing the intrinsic numerical difficulties pertaining to the classical DDCM framework.