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Neural network approach for solving nonlinear eigenvalue problems of structural dynamics

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Abstract

This article introduces a novel connectionist approach for dynamic analysis of structural problem. In general, dynamic analysis of structures leads to eigenvalue problems. Sometimes these eigenvalue problems may be nonlinear eigenvalue problem, which may be difficult to address by traditional methods. As such, we have proposed here an artificial neural network (ANN)-based method to handle nonlinear eigenvalue problems. A four-layer ANN architecture has been constructed for handling the eigenvalue problems, and detailed ANN procedure has been included for clear understanding. Two example problems of overdamped spring mass system have been addressed to show the efficacy of the proposed method. Further, convergence plots and tables for different eigenvalues have also been included to validate the proposed ANN procedure.

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Authors and Affiliations

  1. Department of Mathematics, National Institute of Technology Rourkela, Rourkela, Odisha, 769008, India

    S. K. Jeswal & S. Chakraverty

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  1. S. K. Jeswal
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  2. S. Chakraverty
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Correspondence to S. Chakraverty.

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Jeswal, S.K., Chakraverty, S. Neural network approach for solving nonlinear eigenvalue problems of structural dynamics. Neural Comput & Applic 32, 10669–10677 (2020). https://doi.org/10.1007/s00521-019-04600-3

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