Monotonicity-preserving finite element schemes based on differentiable nonlinear stabilization

TL;DR

This paper introduces a differentiable nonlinear stabilization scheme for finite element methods solving scalar conservation laws, enabling efficient Newton-based solvers and preserving key physical properties like positivity and maximum principles.

Contribution

It develops a smooth, twice differentiable stabilization scheme based on a shock detector, improving convergence rates and solver efficiency over traditional non-smooth methods.

Findings

Newton's method with the smooth scheme reduces iterations by 10-20 times.

The scheme preserves positivity, maximum principle, and linearity.

Projected nonlinear solvers ensure solutions meet monotonicity constraints.

Abstract

In this work, we propose a nonlinear stabilization technique for scalar conservation laws with implicit time stepping. The method relies on an artificial diffusion method, based on a graph-Laplacian operator. It is nonlinear, since it depends on a shock detector. The same shock detector is used to gradually lump the mass matrix. The resulting method is LED, positivity preserving, linearity preserving, and also satisfies a global DMP. Lipschitz continuity has also been proved. However, the resulting scheme is highly nonlinear, leading to very poor nonlinear convergence rates. We propose a smooth version of the scheme, which leads to twice differentiable nonlinear stabilization schemes. It allows one to straightforwardly use Newton's method and obtain quadratic convergence. In the numerical experiments, steady and transient linear transport, and transient Burgers' equation have been considered in 2D. Using the Newton method with a smooth version of the scheme we can reduce 10 to 20 times the number of iterations of Anderson acceleration with the original non-smooth scheme. In any case, these properties are only true for the converged solution, but not for iterates. In this sense, we have also proposed the concept of projected nonlinear solvers, where a projection step is performed at the end of every nonlinear iteration onto a FE space of admissible solutions. The space of admissible solutions is the one that satisfies the desired monotonic properties (maximum principle or positivity).