Exponential Adams Bashforth integrators for stiff ODEs, application to cardiac electrophysiology
Yves Coudi\`ere (IMB, LIRYC, CARMEN), Charlie Douanla Lontsi (IMB,, LIRYC, CARMEN), Charles Pierre (LMAP)

TL;DR
This paper investigates exponential Adams Bashforth integrators for stiff ODEs in cardiac electrophysiology, demonstrating their stability and efficiency comparable to implicit methods through theoretical analysis and numerical experiments.
Contribution
It introduces a new stability analysis framework for EAB integrators with a variable stabilizer, showing their A(alpha)-stability under certain conditions.
Findings
EAB methods are as stable as implicit solvers.
EAB methods are computationally cheaper at similar accuracy.
Numerical experiments confirm the effectiveness of EAB in cardiac models.
Abstract
Models in cardiac electrophysiology are coupled systems of reaction diffusion PDE and of ODE. The ODE system displays a very stiff behavior. It is non linear and its upgrade at each time step is a preponderant load in the computational cost. The issue is to develop high order explicit and stable methods to cope with this situation.In this article, is is analyzed the resort to exponential Adams Bashforth (EAB) integrators in cardiac electrophysiology. The method is presented in the framework of a general and varying stabilizer, that is well suited in this context. Stability under perturbation (or 0-stability) is proven. It provides a new approach for the convergence analysis of the method. The Dahlquist stability properties of the method is performed. It is presented in a new framework that incorporates the discrepancy between the stabilizer and the system Jacobian matrix. Provided this…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNumerical methods for differential equations · Differential Equations and Numerical Methods · Model Reduction and Neural Networks
