Energy and implicit discretization of the Fokker-Planck and Keller-Segel type equations

TL;DR

This paper develops and analyzes finite-volume numerical schemes for the Keller-Segel equation with sensitivity saturation, ensuring preservation of key properties like energy dissipation, positivity, and mass conservation, crucial for accurate pattern formation simulations.

Contribution

It introduces three novel finite-volume schemes based on gradient flow and exponential rewriting, with rigorous proofs of their well-posedness and property preservation.

Findings

Schemes effectively distinguish steady states from transient and artifact states.

Upwind discretization is essential for property preservation at the semi-discrete level.

Numerical results demonstrate the advantages of each proposed method.

Abstract

The parabolic-elliptic Keller-Segel equation with sensitivity saturation, because of its pattern formation ability, is a challenge for numerical simulations. We provide two finite-volume schemes whose goals are to preserve, at the discrete level, the fundamental properties of the solutions, namely energy dissipation, steady states, positivity and conservation of total mass. These requirements happen to be critical when it comes to distinguishing between discrete steady states, Turing unstable transient states, numerical artifacts or approximate steady states as obtained by a simple upwind approach. These schemes are obtained either by following closely the gradient flow structure or by a proper exponential rewriting inspired by the Scharfetter-Gummel discretization. An interesting feature is that upwind is also necessary for all the expected properties to be preserved at the semi-discrete level. These schemes are extended to the fully discrete level and this leads us to tune precisely the terms according to explicit or implicit discretizations. Using some appropriate monotony properties (reminiscent of the maximum principle), we prove well-posedness for the scheme as well as all the other requirements. Numerical implementations and simulations illustrate the respective advantages of the three methods we compare.