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

DOI10.3934/mbe.2021421zbMath1505.92022OpenAlexW3202076746MaRDI QIDQ2092227

Sunwoo Hwang, Seongwon Lee, Hyung-Ju Hwang

Publication date: 2 November 2022

Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mbe.2021421

zbMATH Keywords

neural networkapproximate solutionone-dimensional chemotaxis

Mathematics Subject Classification ID

Computational learning theory (68Q32) Artificial neural networks and deep learning (68T07) PDEs in connection with biology, chemistry and other natural sciences (35Q92) Cell movement (chemotaxis, etc.) (92C17)

Uses Software

• CLAWPACK

Cites Work

• Initiation of slime mold aggregation viewed as an instability
• Model for chemotaxis
• Traveling bands of chemotactic bacteria: a theoretical analysis
• Fourier transform restriction phenomena for certain lattice subsets and applications to nonlinear evolution equations. II: The KdV-equation
• Chemotaxis and chemokinesis in eukaryotic cells: The Keller-Segel equations as an approximation to a detailed model
• On long time asymptotics of the Vlasov-Fokker-Planck equation and of the Vlasov-Poisson-Fokker-Planck system with Coulombic and Newtonian potentials
• Trend to equilibrium for the kinetic Fokker-Planck equation via the neural network approach
• Deep neural network approach to forward-inverse problems
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Simultaneous approximations of multivariate functions and their derivatives by neural networks with one hidden layer
• Random walk with persistence and external bias
• Asymptotic Behavior of an Initial-Boundary Value Problem for the Vlasov--Poisson--Fokker--Planck System
• Solving high-dimensional partial differential equations using deep learning
• Fractional step methods applied to a chemotaxis model

This page was built for publication: Neural network approach to data-driven estimation of chemotactic sensitivity in the Keller-Segel model