Multi-layer Perceptron Estimator for the Total Variation Bounded Constant in Limiters for Discontinuous Galerkin Methods

Abstract

The discontinuous Galerkin (DG) method is widely used in numerical solution of partial differential equations, especially for hyperbolic equations. However, for problems containing strong shocks, the DG method often needs to be supplemented by a limiter to control spurious oscillations and to ensure nonlinear stability. The total variation bounded (TVB) limiter is a popular choice and can maintain the original high order accuracy of the DG scheme in smooth regions and keep a sharp and non-oscillatory discontinuity transition, when a certain TVB constant M is chosen adequately. For scalar conservation laws, suitable choice of this constant M can be based on solid mathematical analysis. However, for nonlinear hyperbolic systems, there is no rigorous mathematical guiding principle for the determination of this constant, and numerical experiments often use ad hoc choices based on experience and through trial and error. In this paper, we develop a TVB constant artificial neural network (ANN) based estimator by constructing a multi-layer perceptron (MLP) model. We generate the training data set by constructing piecewise smooth functions containing local maxima, local minima, and discontinuities. By using the supervised learning strategy, the MLP model is trained offline. The proposed method gives the TVB constant M with robust performance to capture sharp and non-oscillatory shock transitions while maintaining the original high order accuracy in smooth regions. Numerical results using this new estimator in the TVB limiter for DG methods in one and two dimensions are given, and its performance is compared with the classical ad hoc choices of this TVB constant.