Controlling oscillations in spectral methods by local artificial viscosity governed by neural networks
| Autor: | Lukas Schwander, Deep Ray, Jan S. Hesthaven |
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| Předmět: |
Physics and Astronomy (miscellaneous)
Computer science gibbs oscillations artificial viscosity Context (language use) 010103 numerical & computational mathematics 01 natural sciences symbols.namesake spectral methods Entropy (information theory) Applied mathematics 0101 mathematics Numerical Analysis Conservation law Artificial neural network Applied Mathematics neural networks Computer Science Applications Euler equations 010101 applied mathematics Computational Mathematics Nonlinear system Modeling and Simulation Viscosity (programming) symbols conservation laws Spectral method |
| Popis: | While a nonlinear viscosity is used widely to control oscillations when solving conservation laws using high-order elements based methods, such techniques are less straightforward to apply in global spectral methods since a local estimate of the solution regularity is generally required. In this work we demonstrate how to train and use a local artificial neural network to estimate the local solution regularity and demonstrate the efficiency of nonlinear artificial viscosity methods based on this, in the context of Fourier spectral methods. We compare with entropy viscosity techniques and illustrate the promise of the neural network based estimators when solving one- and two-dimensional conservation laws, including the Euler equations. (C) 2021 Elsevier Inc. All rights reserved. |
| Databáze: | OpenAIRE |
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