Euler iteration augmented physics-informed neural networks for time-varying parameter estimation of the epidemic compartmental model

HIGHLIGHTS

• who: December and collaborators from the University of Su00e3o Paulo, Brazil have published the paper: Euler iteration augmented physics-informed neural networks for time-varying parameter estimation of the epidemic compartmental model, in the Journal: (JOURNAL) of 11/03/2020
• what: The authors propose an Euler iteration augmented physicsinformed neural_networks(called Euler-PINNs) to optimally integrates realworld reported data, epidemic laws and deep neural_networks to capture the dynamics of COVID-19. The authors evaluate how the estimated parameters fit the SIRD compartmental model by comparing the results of previous publications. The authors compare the results . . .
  

   

  

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