A hybrid proper orthogonal decomposition and next generation reservoir computing approach for high-dimensional chaotic prediction: Application to flow-induced vibration of tube bundles
To address the significant challenges in predicting high-dimensional chaotic systems, this paper introduces a novel hybrid strategy that combines proper orthogonal decomposition (POD), which serves as reduced order modeling (ROM), with next generation reservoir computing (NGRC), a data-driven prediction model. The POD-NGRC strategy harnesses the strengths of POD in extracting principal evolutionary features and reducing system complexity, along with the high accuracy, ease of design, enhanced robustness, and high computational efficiency offered by NGRC.