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

Enrico Schiassi, Mario De Florio, Andrea D’Ambrosio, Daniele Mortari and Roberto Furfaro
Additional contact information
Enrico Schiassi: Systems & Industrial Engineering, University of Arizona, Tucson, AZ 85721, USA
Mario De Florio: Systems & Industrial Engineering, University of Arizona, Tucson, AZ 85721, USA
Andrea D’Ambrosio: Systems & Industrial Engineering, University of Arizona, Tucson, AZ 85721, USA
Daniele Mortari: Aerospace Engineering, Texas A&M University, College Station, TX 77843-3141, USA
Roberto Furfaro: Systems & Industrial Engineering, University of Arizona, Tucson, AZ 85721, USA

Mathematics, 2021, vol. 9, issue 17, 1-17

Abstract: 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.

Keywords: Physics-Informed Neural Networks; functional interpolation; Theory of Functional Connections; Extreme Learning Machine; epidemiological compartmental models; COVID-19 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/17/2069/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/17/2069/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:17:p:2069-:d:623012

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().