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

Abstract

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 CNNConvLSTM 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 CNNConvLSTM in terms of shorter training time and lower training and testing loss. The CNNConvLSTM model can provide useful guidance when investigating chaotic vibrations for performance enhancement and design optimization of microbeam-related devices.