Document Type
Article
Publication Date
6-21-2023
Abstract
A machine learning procedure is proposed to create numerical schemes for solutions of nonlinear wave equations on coarse grids. This method trains stencil weights of a discretization of the equation, with the truncation error of the scheme as the objective function for training. The method uses centered finite differences to initialize the optimization routine and a second-order implicit-explicit time solver as a framework. Symmetry conditions are enforced on the learned operator to ensure a stable method. The procedure is applied to the Korteweg–de Vries equation. It is observed to be more accurate than finite difference or spectral methods on coarse grids when the initial data is near enough to the training set.
Source Publication
Mathematics
Recommended Citation
Williams, K. O. F., & Akers, B. F. (2023). Numerical Simulation of the Korteweg–de Vries Equation with Machine Learning. Mathematics, 11(13), 2791. https://doi.org/10.3390/math11132791
Comments
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Author marked [*] enrolled as an AFIT graduate student at the time of publication.
Funding note: Author B. Akers acknowledges funding from the APTAWG, under the program “Simulation of laser propagation in reactive media.”