Error control for statistical solutions of hyperbolic systems of conservation laws

Statistical solutions have recently been introduced as an alternative solution framework for hyperbolic systems of conservation laws. In this work, we derive a novel a posteriori error estimate in the Wasserstein distance between dissipative statistical solutions and numerical approximations obtained from the Runge-Kutta Discontinuous Galerkin method in one spatial dimension, which rely on so-called regularized empirical measures. The error estimator can be split into deterministic parts which correspond to spatio-temporal approximation errors and a stochastic part which reflects the stochastic error. We provide numerical experiments which examine the scaling properties of the residuals and verify their splitting.

Automated summary

The paper introduces a new error estimation method in the Wasserstein distance between dissipative statistical solutions and numerical approximations, and splits the error estimator into deterministic and stochastic parts. Numerical experiments conducted verify the scaling properties of the residuals and the accuracy of the splitting.