A Least-Squares Finite Element Method Based on the Helmholtz Decomposition for Hyperbolic Balance Laws

Abstract

In this paper, a least-squares finite element method for scalar nonlinear hyperbolic balance laws is proposed and studied. The approach is based on a formulation that utilizes an appropriate Helmholtz decomposition of the flux vector and is related to the standard notion of a weak solution. This relationship, together with a corresponding connection to negative-norm least-squares, is described in detail. As a consequence, an important numerical conservation theorem is obtained, similar to the famous Lax-Wendroff theorem. The numerical conservation properties of the method in this paper do not fall precisely in the framework introduced by Lax and Wendroff, but they are similar in spirit as they guarantee that when L 2 convergence holds, the resulting approximations approach a weak solution to the hyperbolic problem. The least-squares functional is continuous and coercive in an H −1 -type norm, but not L 2 -coercive. Nevertheless, the L 2 convergence properties of the method are discussed. Convergence can be obtained either by an explicit regularization of the functional, that provides control of the L 2 norm, or by properly choosing the finite element spaces, providing implicit control of the L 2 norm. Numerical results for the inviscid Burgers equation with discontinuous source terms are shown, demonstrating the L 2 convergence of the obtained approximations to the physically admissible solution. The numerical method utilizes a least-squares functional, minimized on finite element spaces, and a Gauss-Newton technique with nested iteration. We believe that the linear systems encountered with this formulation are amenable to multigrid techniques and combining the method with adaptive mesh refinement would make this approach an efficient tool for solving balance laws (this is the focus of a future study).

Least-Squares ReLU Neural Network (LSNN) Method For Scalar Nonlinear Hyperbolic Conservation Law

“…To enforce this condition weakly, [8] introduced an independent variable, the spatial-temporal flux, for the inviscid Burgers equation and applied the least-squares principle to the resulting equivalent system. A variant of this method was also studied in [8,18] by using the Helmholtz decomposition of the flux.…”

Finite Volume Least-Squares Neural Network (FV-LSNN) Method for Scalar Nonlinear Hyperbolic Conservation Laws

“…For the inviscid Burgers equation, a least-squares formulation was developed to enforce the RH relation weakly by introducing the spatial-temporal flux as an independent variable in [10]; a variant of this method was also studied in [10,18] by using the Helmholtz decomposition of the flux. Least-squares methods of this type have an additional term in the corresponding functionals comparing to the direct application of the least-squares principle to the original PDE.…”