Deep auto-encoder in model reduction of lage-scale spatiotemporal dynamics

摘要

This paper presents a deep auto-encoder based model reduction method for large scale spatiotemporal process. This method includes three phases in order to find the near-optimal parameters of the reduced order model. The sequence of the phases is allocated according to the idea of greedy training which approximately minimizes the modeling error. This method also avoids including the spatial dimensionality into the model which enables it to handle large-scale model reduction. Two case studies are carried out to demonstrate the effectiveness of the method.