Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems

Niels Hørbye Christiansen, Jan Becker Høgsberg, Ole Winther

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is able to perform accurate response prediction much faster than the corresponding finite element model. Initial result indicate a reduction in cpu time by two orders of magnitude.
Original languageEnglish
Title of host publicationProceedings of the 24th Nordic Seminar on Computational Mechanics
Publication date2011
Publication statusPublished - 2011
EventNSCM-24 - Helsinki, Finland
Duration: 1 Jan 2011 → …

Conference

ConferenceNSCM-24
CityHelsinki, Finland
Period01/01/2011 → …

Keywords

  • Nonlinear structural dynamics
  • Artificial neural networks

Cite this

Christiansen, N. H., Høgsberg, J. B., & Winther, O. (2011). Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems. In Proceedings of the 24th Nordic Seminar on Computational Mechanics
Christiansen, Niels Hørbye ; Høgsberg, Jan Becker ; Winther, Ole. / Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems. Proceedings of the 24th Nordic Seminar on Computational Mechanics. 2011.
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Christiansen, NH, Høgsberg, JB & Winther, O 2011, Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems. in Proceedings of the 24th Nordic Seminar on Computational Mechanics. NSCM-24, Helsinki, Finland, 01/01/2011.

Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems. / Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole.

Proceedings of the 24th Nordic Seminar on Computational Mechanics. 2011.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AB - It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is able to perform accurate response prediction much faster than the corresponding finite element model. Initial result indicate a reduction in cpu time by two orders of magnitude.

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Christiansen NH, Høgsberg JB, Winther O. Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems. In Proceedings of the 24th Nordic Seminar on Computational Mechanics. 2011