基于卷积 LSTM-注意力的编码器-解码器神经网络用于预测多维动态系统的混沌振动

IF 3 3区工程技术 Q2 ENGINEERING, CIVIL

International Journal of Structural Stability and Dynamics Pub Date : 2023-12-01 DOI:10.1142/s0219455424502274

Luyao Wang, Liming Dai, Haixing Zhao, Pan Fang

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Convolutional LSTM-attention based encoder-decoder neural network for prediction of chaotic vibrations of multi-dimensional dynamic systems

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来源期刊

International Journal of Structural Stability and Dynamics 工程技术-工程：机械

CiteScore

5.30

自引率

38.90%

发文量

291

审稿时长

4 months

期刊介绍： The aim of this journal is to provide a unique forum for the publication and rapid dissemination of original research on stability and dynamics of structures. Papers that deal with conventional land-based structures, aerospace structures, marine structures, as well as biostructures and micro- and nano-structures are considered. Papers devoted to all aspects of structural stability and dynamics (both transient and vibration response), ranging from mathematical formulations, novel methods of solutions, to experimental investigations and practical applications in civil, mechanical, aerospace, marine, bio- and nano-engineering will be published. The important subjects of structural stability and structural dynamics are placed together in this journal because they share somewhat fundamental elements. In recognition of the considerable research interests and recent proliferation of papers in these subjects, it is hoped that the journal may help bring together papers focused on related subjects, including the state-of-the-art surveys, so as to provide a more effective medium for disseminating the latest developments to researchers and engineers. This journal features a section for technical notes that allows researchers to publish their initial findings or new ideas more speedily. Discussions of papers and concepts will also be published so that researchers can have a vibrant and timely communication with others.