文献类型：期刊文献

英文题名：Time-space coupled learning method for model reduction of distributed parameter systems with encoder-decoder and RNN

作者：Qing, Xiangyun[1];Jin, Jing[1];Niu, Yugang[1];Zhao, Shuangliang[2,3]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Chem Engn, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Sch Chem Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：66

期号：8

外文期刊名：AICHE JOURNAL

收录：;EI(收录号：20202008665773);WOS:【SCI-EXPANDED(收录号:WOS:000532817800001)】；

基金：National Natural Science Foundation of China, Grant/Award Numbers: 21878078, 91934302

语种：英文

外文关键词：deep learning; distributed parameter system; model reduction; recurrent neural network; spatiotemporal dynamics

摘要：Model reduction of a high-dimensional distributed parameter system (DPS) reduces the complexity of the system for various applications, from monitoring to model predictive control, while retaining its intrinsic properties. Unfortunately, the assumption of time-space separability usually fails to hold for popular time-space separation model reduction methods because the space and time of the DPS are inherently coupled. In this study, a time-space coupled learning method for a data-driven model reduction of the DPS is presented. The proposed method has the advantage of preserving the time-space coupling characteristics and increasing the number of degrees of freedom during the model reduction learning process. A novel deep-learning architecture is presented by combining encoder-decoder networks with recurrent neural networks. Given a high-dimensional system without an exact partial differential equation description, the dimension-reduced model and its temporal dynamics are jointly learned using the collected input and output data. The learned model is then applied to predict the low-dimensional representations and reconstruct the high-dimensional outputs. The proposed method was demonstrated on the catalytic rod in a tubular reactor with recycle, the results of which indicate a better modeling accuracy and lower intrinsic dimensionality compared with classical time-space separation model reduction methods.