Parameter Identification in Manufacturing Systems Using Physics-Informed Neural Networks
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-57496-2_6
Reference16 articles.
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3. Diagne, M., Karafyllis, I.: Event-triggered boundary control of a continuum model of highly re-entrant manufacturing systems. Automatica 134, 109902 (2021). https://doi.org/10.1016/j.automatica.2021.109902
4. Göttlich, S., Herty, M., Klar, A.: Modelling and optimization of supply chains on complex networks. Commun. Math. Sci. 4(2), 315–330 (2006). https://doi.org/10.4310/cms.2006.v4.n2.a3, https://doi.org/10.4310%2Fcms.2006.v4.n2.a3
5. Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S., Yang, L.: Physics-informed machine learning. Nat. Rev. Phys. 3(6), 422–440 (2021). https://doi.org/10.1038/s42254-021-00314-5
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