A novel growing wavelet neural network algorithm for solving chemotaxis systems with blow‐up

DOI10.1002/mma.9449OpenAlexW4381852607MaRDI QIDQ6183137

No author found.

Publication date: 26 January 2024

Published in: Mathematical Methods in the Applied Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/mma.9449

zbMATH Keywords

finite time blow-upartificial intelligence methodswavelet neural networkchemotaxis systemgrowing algorithm

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Numerical methods for initial value problems involving ordinary differential equations (65L05)

Cites Work

• Unnamed Item
• Unnamed Item
• A well-balanced finite volume scheme for a mixed hyperbolic/parabolic system to model chemotaxis
• A recurrent neural network for solving a class of generalized convex optimization problems
• Initiation of slime mold aggregation viewed as an instability
• Model for chemotaxis
• Traveling bands of chemotactic bacteria: a theoretical analysis
• From 1970 until present: The Keller-Segel model in chemotaxis and its consequences. I
• Machine learning of linear differential equations using Gaussian processes
• From 1970 until present: the Keller-Segel model in chemotaxis and its consequences. II
• Global existence and blowup of solutions to a chemotaxis system.
• Upwind-difference potentials method for Patlak-Keller-Segel chemotaxis model
• Novel design of morlet wavelet neural network for solving second order Lane-Emden equation
• Energy dissipative local discontinuous Galerkin methods for Keller-Segel chemotaxis model
• Keller-Segel chemotaxis models: a review
• Adaptive moving mesh upwind scheme for the two-species chemotaxis model
• Linearized implicit methods based on a single-layer neural network: application to Keller-Segel models
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• A positivity preserving moving mesh finite element method for the Keller-Segel chemotaxis model
• Efficient numerical schemes for the solution of generalized time fractional Burgers type equations
• Numerical study of two-species chemotaxis models
• A new steepest descent differential inclusion-based method for solving general nonsmooth convex optimization problems
• Wavelet neural networks: a practical guide
• Discontinuous Galerkin methods for the chemotaxis and haptotaxis models
• Singularity patterns in a chemotaxis model
• DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations
• A First Course in the Numerical Analysis of Differential Equations
• Global Behaviour of a Reaction-Diffusion System Modelling Chemotaxis
• Convergence of a method based on the exponential integrator and Fourier spectral discretization for stiff stochastic PDEs
• Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
• Numerical Methods for Ordinary Differential Equations
• DeepBHCP: deep neural network algorithm for solving backward heat conduction problems
• Radial basis function neural network (RBFNN) approximation of Cauchy inverse problems of the Laplace equation
• DNN-HDG: a deep learning hybridized discontinuous Galerkin method for solving some elliptic problems

This page was built for publication: A novel growing wavelet neural network algorithm for solving chemotaxis systems with blow‐up