Convolutional LSTM-Attention Based Encoder-Decoder Neural Network for Prediction of Chaotic Vibrations of Multi-Dimensional Dynamic Systems

The present research proposes a convolutional long-short term memory (ConvLSTM) with an attention mechanism (AM) model, termed as ConvLSTM-AM, to conduct prediction of chaotic vibrations of multi-dimensional dynamic systems. The proposed data-driven model is based on an encoder-decoder architecture where the lengths of inputs and outputs are variable. Different from other conventional benchmarks which consider the temporal correlation solely to deal with the chaotic sequences, this research work takes the spatial information into account. In this sense, the ConvLSTM is adopted as an encoder to acquire useful chaotic spatiotemporal patterns and retain long-term successive dependencies. LSTM and AM in this research are taken as the main structures of the procedure in decoding, in which the LSTM is used as the further temporal processor and AM is stacked on the top to exploit more salient information of the historical data. Among that, a residual connection between the outputs of LSTM and the information of attention is considered in AM to prevent gradient vanishment. Two datasets of chaotic vibrations of multi-dimensional systems are employed to adequately illustrate the effectiveness and feasibility of the proposed model. Besides, five conventional benchmarks are built to demonstrate the advantages of the proposed model in terms of both training and generalization performance. As found in the research, the training time is reduced with lower testing loss in comparing with the other five counterparts, as the spatial information introduced expedites the training convergence. The present research provides a useful guidance for predicting and analysing chaotic vibrations of multi-dimensional dynamic systems.