Data-driven nonintrusive reduced order modeling for dynamical systems with moving boundaries using Gaussian process regression

We present a data-driven nonintrusive model order reduction method for dynamical systems with moving boundaries. The proposed method draws on the proper orthogonal decomposition, Gaussian process regression, and moving least squares interpolation. It combines several attributes that are not simultaneously satisfied in the existing model order reduction methods for dynamical systems with moving boundaries. Specifically, the method requires only snapshot data of state variables at discrete time instances and the parameters that characterize the boundaries, but not further knowledge of the full-order model and the underlying governing equations. The dynamical systems can be generally nonlinear. The movements of boundaries are not limited to prescribed or periodic motions but can be free motions. In addition, we numerically investigate the ability of the reduced order model constructed by the proposed method to forecast the full-order solutions for future times beyond the range of snapshot data. The error analysis for the proposed reduced order modeling and the criteria to determine the furthest forecast time are also provided. Through numerical experiments, we assess the accuracy and efficiency of the proposed method in several benchmark problems. The snapshot data used to construct and validate the reduced order model are from analytical/numerical solutions and experimental measurements. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The proposed data-driven nonintrusive model order reduction method for dynamical systems with moving boundaries combines several attributes and can accurately forecast solutions beyond the range of snapshot data.