Neural network approach to data-driven estimation of chemotactic sensitivity in the Keller-Segel model

• Published in: Mathematical biosciences and engineering : MBE Vol. 18; no. 6; pp. 8524 - 8534
• Main Authors: Hwang, Sunwoo, Lee, Seongwon, Hwang, Hyung Ju
• Format: Journal Article
• Language: English
• Published: United States AIMS Press 01.01.2021
• Subjects: approximated solution; artificial neural networks; Chemotaxis; differential equation; Models, Biological; Neural Networks, Computer; patlak-keller-segel equation; Population Dynamics; artificial neural networks; differential equation; approximated solution; Patlak-Keller-Segel equation
• ISSN: 1551-0018; 1551-0018
• DOI: 10.3934/mbe.2021421

We consider the mathematical model of chemotaxis introduced by Patlak, Keller, and Segel. Aggregation and progression waves are present everywhere in the population dynamics of chemotactic cells. Aggregation originates from the chemotaxis of mobile cells, where cells are attracted to migrate to higher concentrations of the chemical signal region produced by themselves. The neural net can be used to find the approximate solution of the PDE. We proved that the error, the difference between the actual value and the predicted value, is bound to a constant multiple of the loss we are learning. Also, the Neural Net approximation can be easily applied to the inverse problem. It was confirmed that even when the coefficient of the PDE equation was unknown, prediction with high accuracy was achieved.