A Hybrid Finite-Difference-Particle Method for Chemotaxis Models
<|reference_start|>A Hybrid Finite-Difference-Particle Method for Chemotaxis Models: Chemotaxis systems play a crucial role in modeling the dynamics of bacterial and cellular behaviors, including propagation, aggregation, and pattern formation, all under the influence of chemical signals. One notable characteristic of these systems is their ability to simulate concentration phenomena, where cell density undergoes rapid growth near specific concentration points or along certain curves. Such growth can result in singular, spiky structures and lead to finite-time blowups.   Our investigation focuses on the dynamics of the Patlak-Keller-Segel chemotaxis system and its two-species extensions. In the latter case, different species may exhibit distinct chemotactic sensitivities, giving rise to very different rates of cell density growth. Such a situation may be extremely challenging for numerical methods as they may fail to accurately capture the blowup of the slower-growing species mainly due to excessive numerical dissipation.   In this paper, we propose a hybrid finite-difference-particle (FDP) method, in which a particle method is used to solve the chemotaxis equation(s), while finite difference schemes are employed to solve the chemoattractant equation. Thanks to the low-dissipation nature of the particle method, the proposed hybrid scheme is particularly adept at capturing the blowup behaviors in both one- and two-species cases. The proposed hybrid FDP methods are tested on a series of challenging examples, and the obtained numerical results demonstrate that our hybrid method can provide sharp resolution of the singular structures even with a relatively small number of particles. Moreover, in the two-species case, our method adeptly captures the blowing-up solution for the component with lower chemotactic sensitivity, a feature not observed in other works.<|reference_end|>
@article{chertock2024a,
    title={A Hybrid Finite-Difference-Particle Method for Chemotaxis Models},
    author={Alina Chertock, Shumo Cui, Alexander Kurganov, and Chenxi Wang},
    journal={arXiv preprint arXiv:2402.02808},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02808},
    primaryClass={math.NA cs.NA physics.bio-ph q-bio.QM}
}