ConvLSTM-based spatiotemporal and temporal processing models for chaotic vibration prediction of a microbeam.

• This work introduces a novel application of a hybrid data-driven model combining convolutional neural networks (CNN) and convolutional long short-term memory (ConvLSTM) for predicting chaotic vibrations in microbeams. • The proposed CNN ConvLSTM model excels in both spatiotemporal and temporal processing. • The prediction task is in a completely data-driven and non-intrusive manner. • The CNN ConvLSTM model demonstrates superior performance with shorter training times and lower training and testing losses. The current work proposes a hybrid data-driven model, consisting of convolutional neural networks (CNN) and convolutional long short-term memory (ConvLSTM), to enable an innovative application in prediction for microbeam's chaotic vibrations. In the proposed CNN ConvLSTM model, CNN is used as feature extractor while ConvLSTM retains long-term connectivity across the entire sequence of frames. The flexibility and effectiveness of the model are demonstrated by its capability to perform both spatiotemporal and temporal processing. Specifically, the spatiotemporal model predicts the chaotic vibrations at 19 selected locations within the microbeam at a specific instant, while the temporal model predicts the microbeam's chaotic vibrations at a fixed location over time. Additionally, two conventional spatiotemporal models and three conventional temporal models are built for comparison, demonstrating the superior performance of CNN ConvLSTM in terms of shorter training time and lower training and testing loss. The CNN ConvLSTM model can provide useful guidance when investigating chaotic vibrations for performance enhancement and design optimization of microbeam-related devices. [ABSTRACT FROM AUTHOR]

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