Physics-Informed Neural Networks and Functional Interpolation for Data-Driven Parameters Discovery of Epidemiological Compartmental Models Academic Article uri icon


  • In this work, we apply a novel and accurate Physics-Informed Neural Network Theory of Functional Connections (PINN-TFC) based framework, called Extreme Theory of Functional Connections (X-TFC), for data-physics-driven parameters discovery of problems modeled via Ordinary Differential Equations (ODEs). The proposed method merges the standard PINNs with a functional interpolation technique named Theory of Functional Connections (TFC). In particular, this work focuses on the capability of X-TFC in solving inverse problems to estimate the parameters governing the epidemiological compartmental models via a deterministic approach. The epidemiological compartmental models treated in this work are Susceptible-Infectious-Recovered (SIR), Susceptible-Exposed-Infectious-Recovered (SEIR), and Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS). The results show the low computational times, the high accuracy, and effectiveness of the X-TFC method in performing data-driven parameters discovery systems modeled via parametric ODEs using unperturbed and perturbed data.

published proceedings


altmetric score

  • 0.5

author list (cited authors)

  • Schiassi, E., De Florio, M., D'Ambrosio, A., Mortari, D., & Furfaro, R.

citation count

  • 8

complete list of authors

  • Schiassi, Enrico||De Florio, Mario||D'Ambrosio, Andrea||Mortari, Daniele||Furfaro, Roberto

publication date

  • January 2021