An edge detector based on artificial neural network with application to hybrid compact-WENO finite difference scheme

DOI10.1007/s10915-020-01237-6zbMath1444.76077OpenAlexW2996306504MaRDI QIDQ2187030

Zhen Gao, Xiao Wen, Jan S. Hesthaven, Wai-Sun Don

Publication date: 10 June 2020

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://infoscience.epfl.ch/record/271422/files/ANN-0928.pdf

zbMATH Keywords

shock wavehyperbolic conservation lawback propagation neural networkdam breaking

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Shock waves and blast waves in fluid mechanics (76L05) Water waves, gravity waves; dispersion and scattering, nonlinear interaction (76B15) Finite difference methods applied to problems in fluid mechanics (76M20) Gas dynamics (general theory) (76N15)

Related Items (9)

A data-driven high order sub-cell artificial viscosity for the discontinuous Galerkin spectral element method ⋮ A characteristic-featured shock wave indicator on unstructured grids based on training an artificial neuron ⋮ A class of high-order finite difference schemes with minimized dispersion and adaptive dissipation for solving compressible flows ⋮ A simplified multilayer perceptron detector for the hybrid WENO scheme ⋮ A hybrid WENO scheme for steady-state simulations of Euler equations ⋮ A Hybrid WENO Scheme for Steady Euler Equations in Curved Geometries on Cartesian Grids ⋮ A Shock Sensor Based on Image Segmentation with Application to a Hybrid Central/WENO Scheme ⋮ A new hybrid WENO scheme with the high-frequency region for hyperbolic conservation laws ⋮ Convolution Neural Network Shock Detector for Numerical Solution of Conservation Laws

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