A new nonlinear parameterized model order reduction technique combining the interpolation method and Proper Orthogonal Decomposition

A parameterized model order reduction technique for nonlinear system is presented in this paper, which combines the interpolation method with the Proper Orthogonal Decomposition (POD). The efficiency of the proposed approach lies in the use of interpolation method which reduces the complexity of POD in representing parameterized nonlinear functions. In order to capture the accuracy of the parameterized reduced model over a large range of parameter values, a training scheme is proposed to automatically select the training parameter points by the greedy sampling method. The results show that the accuracy and efficacy are improved in the proposed nonlinear parameterized reduction method.