Derivative-based new upper bound of Sobol' sensitivity measure

摘要

Global sensitivity (also called "uncertainty importance measure") can reflect the effect of input variables on output response. The variance-based importance measure proposed by Sobol' has highly general applicability. The Sobol' total sensitivity index S(i)(tot)can estimate the total contribution of input variables to the model output, including the self-influence of variable and the intercross influence of variable vectors. However, the computational load of S-i(tot) is extremely heavy for double-loop simulation. The main sensitivity index S-i is the lower bound of S-i(tot), and new upper bounds of S-i(tot) based derivative are derived and proposed. New upper bounds of S-i(tot) for different variable distribution types (such as uniform, normal, exponential, triangular, beta and gamma) are analyzed, and the process and formulas are presented comprehensively according to functional analysis and the Euler-Lagrange equation. Derivative-based upper bounds are easy to implement and evaluate numerically. Several numerical and engineering examples are adopted to verify the efficiency and applicability of the presented upper bounds, which can effectively estimate the S-i(tot) value.