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

Shuai Han et al. (2024)

> Home

Details and statistics

DOI: 10.1016/J.JFRANKLIN.2024.106671

access: closed

type: Journal Article

metadata version: 2025-09-28

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - journals/jfi/HanSSWZ24
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/jfi/HanSSWZ24
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)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.