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"Approaching epidemiological dynamics of COVID-19 with physics-informed ..."

Shuai Han et al. (2024)

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DOI: 10.1016/J.JFRANKLIN.2024.106671

access: closed

type: Journal Article

metadata version: 2024-05-31

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Shuai Han, Lukas Stelz, Horst Stoecker, Lingxiao Wang, Kai Zhou:
Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks. J. Frankl. Inst. 361(6): 106671 (2024)

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