Parameter-robust preconditioners for a cell-by-cell poroelasticity model with interface coupling

TL;DR

This paper introduces a scalable, parameter-robust solver for a detailed cell-by-cell poroelasticity model of brain tissue, enabling efficient simulation of cellular interactions and volume regulation in complex biological structures.

Contribution

It develops a novel preconditioning framework ensuring robustness and scalability for a complex, multi-parameter poroelastic model with interface coupling, validated through numerical experiments.

Findings

Preconditioners are robust across all model parameters.

Scalability achieved with Algebraic Multigrid approximations.

Effective handling of diverse boundary conditions.

Abstract

This paper presents a scalable and robust solver for a cell-by-cell poroelasticity model, describing the mechanical interactions between brain cells embedded in extracellular space. Explicitly representing the complex cellular shapes, the proposed approach models both intracellular and extracellular spaces as distinct poroelastic media, separated by a permeable cell membrane which allows hydrostatic and osmotic pressure-driven fluid exchange. Based on a three-field (displacement, total pressure, and fluid pressure) formulation, the solver leverages the framework of norm-equivalent preconditioning and appropriately fitted norms to ensure robustness across all material parameters of the model. Scalability for large and complex geometries is achieved through efficient Algebraic Multigrid (AMG) approximations of the preconditioners' individual blocks. Furthermore, we accommodate diverse boundary conditions, including full Dirichlet boundary conditions for displacement, which we handle efficiently using the Sherman-Morrison-Woodbury formula. Our theoretical analysis is complemented by numerical experiments demonstrating the preconditioners' robustness and performance across various parameters relevant to realistic scenarios. A large scale example of cellular swelling on a dense reconstruction of the mouse visual cortex highlights the method's potential for investigating complex physiological processes such as cellular volume regulation in detailed biological structures.