Data-driven computing in elasticity via kernel regression

Abstract

ABSTRACT: This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set. Keywords: Data-driven computational mechanics, Model-free method, Nonparametric method, Kernel regression, Nadaraya–Watson estimator