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Titlebook: Nonlinear Structures & Systems, Volume 1; Proceedings of the 4 Matthew R.W. Brake,Ludovic Renson,Paolo Tiso Conference proceedings 2024 The

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發(fā)表于 2025-3-21 16:29:36 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Nonlinear Structures & Systems, Volume 1
副標題Proceedings of the 4
編輯Matthew R.W. Brake,Ludovic Renson,Paolo Tiso
視頻videohttp://file.papertrans.cn/668/667704/667704.mp4
叢書名稱Conference Proceedings of the Society for Experimental Mechanics Series
圖書封面Titlebook: Nonlinear Structures & Systems, Volume 1; Proceedings of the 4 Matthew R.W. Brake,Ludovic Renson,Paolo Tiso Conference proceedings 2024 The
描述.Nonlinear Structures & Systems, Volume 1: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the first volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Nonlinear Dynamics, including papers on:.Experimental Nonlinear Dynamics.Jointed Structures: Identification, Mechanics, Dynamics.Nonlinear Damping.Nonlinear Modeling and Simulation.Nonlinear Reduced-Order Modeling.Nonlinearity and System Identification.
出版日期Conference proceedings 2024
關鍵詞Nonlinear dynamics; modal analysis; structural engineering; complexity; Conference Proceedings
版次1
doihttps://doi.org/10.1007/978-3-031-36999-5
isbn_softcover978-3-031-37001-4
isbn_ebook978-3-031-36999-5Series ISSN 2191-5644 Series E-ISSN 2191-5652
issn_series 2191-5644
copyrightThe Society for Experimental Mechanics, Inc. 2024
The information of publication is updating

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Creating Data-Driven Reduced-Order Models for Nonlinear Vibration via Physics-Informed Neural Netwoent cases, but this approach has previously been demonstrated to be overly dependent on the scale of these characteristics, a point that has prevented wider application. In this work, initial steps are taken to utilise physics-informed recurrent neural networks (RNNs) in place of the static step, al
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On the Harmonic Balance Method Augmented with Nonsmooth Basis Functions for Contact/Impact Problemsh dynamical systems. Rather than requiring many harmonics to capture a system response using classical, smooth Fourier terms, the frequency domain discretization could be captured by a combination of a finite Fourier series supplemented with nonsmooth basis functions to improve convergence of the so
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發(fā)表于 2025-3-22 14:43:39 | 只看該作者
Modeling Nonlinear Structures Using Physics-Guided, Machine-Learnt Models, where a neural network is used to directly model the Lagrangian function of the system. To enforce physical consistency, the Euler-Lagrange equations of motion of the system are obtained by differentiating this neural network using automatic differentiation techniques. The potential of this modelin
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Simulating Nonlinear Beating Phenomena Induced by Dry Friction in Dynamic Systems,determine system model variables that may simulate the self-excitation and beat phenomena observed in the structural dynamics. Beat phenomena may also be a result of the existence of two or more closely separated damped natural frequencies. We also investigate the degree to which self-excitation in
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Strategies for Improving the Comparison of Frequency Response Functions with Similarity Metrics,pectra can be applied to make further comparisons. This paper seeks to verify the new methods presented in Manring et al. (J Sound Vib 539:117255, 2022) using a modified experiment and proposes a windowing method as another tool for comparing similar transfer functions. The authors investigate these
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