A cross validation‐Voronoi and entropy based adaptive sampling strategy for uncertainty quantification through hybrid simulation. Issue 3 (2nd November 2022) - Yu, Guangquan; Chen, Cheng; Hou, Hetao; Tian, Li; Xu, Weijie
Abstract: Intensive and complex engineering models drive the need for computational affordability and accuracy. To obtain more accurate prediction, an effective sampling method is proposed and evaluated in this study by integrating CV (cross validation)‐Voronoi (CV‐V) and entropy (EP) strategies. The new CVV (CV‐V) ‐DEP (Distance and EP) method uses CV‐V to divide the sample space into Voronoi units and selects the unit with largest error. The candidate sample point with the largest EP and distance values in the selected unit is then selected. To verify the efficacy of the proposed method, CVV‐DEP is first compared with traditional CV‐V and CVV‐EP (CV‐V and EP) strategies for six nonlinear functions. Computational simulations are further conducted using the proposed method for uncertainty quantification through hybrid simulation of two different nonlinear systems. The proposed method is demonstrated to provide great potential for uncertainty quantification of civil engineering infrastructures through hybrid simulation. Abstract : An effective sampling method is proposed and evaluated in this study by integrating cross validation‐Voronoi (CV‐V) and entropy (EP) strategies. Computational simulations of hybrid tests of two different nonlinear systems are further conducted using the proposed method for uncertainty quantification. The proposed method is demonstrated to provide great potential for uncertainty quantification through hybrid simulation.