Linearized implicit methods based on a single-layer neural network: application to Keller-Segel models
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Publication:2204551
DOI10.1007/s10915-020-01310-0OpenAlexW3016191275MaRDI QIDQ2204551
Publication date: 15 October 2020
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2004.03763
finite volume methodneural networkspartial differential equationslinearized schemeKeller-Segel models
Learning and adaptive systems in artificial intelligence (68T05) Cell movement (chemotaxis, etc.) (92C17) Finite volume methods for initial value and initial-boundary value problems involving PDEs (65M08)
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A linearized decoupled Crank-Nicolson FEM for Keller-Segel chemotactic model with nonlinear secretion ⋮ A novel growing wavelet neural network algorithm for solving chemotaxis systems with blow‐up
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