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Using Hamiltonian Neural Networks to Model Two Coupled Duffing Oscillators

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Abstract

In this short note, the performance of two kinds of physics-guided computing schemes, namely the Hamiltonian Neural Network and the Port-Hamiltonian Neural Network, are discussed through the predicted dynamics of two coupled Duffing oscillators. First, we propose a new error bound which holds for both types of networks. Then, we numerically investigate some alternative activation functions in terms of prediction accuracy. The numerical results show the potential of the approaches when compared to the standard neural networks in the transient regime.

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Acknowledgements

Dr Tomasiello acknowledges funding from the European Social Fund via the IT Academy program and from the Estonian Research Council grant PRG1604.

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Authors and Affiliations

  1. Institute of Physics, University of Tartu, Tartu, Estonia

    Gordei Pribõtkin

  2. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Stefania Tomasiello

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  1. Gordei Pribõtkin
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  2. Stefania Tomasiello
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Correspondence to Stefania Tomasiello.

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Pribõtkin, G., Tomasiello, S. Using Hamiltonian Neural Networks to Model Two Coupled Duffing Oscillators. Neural Process Lett 55, 8163–8180 (2023). https://doi.org/10.1007/s11063-023-11306-0

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