Cbcbeat: an Adjoint-enabled Framework for Computational Cardiac Electrophysiology

By Marie E. Rognes, Patrick E. Farrell, Simon W. Funke, Johan E. Hake, Mary M. Maleckar. 2017

17 citations recorded by the source corpus.

cbcbeat (cbcbeat 2017) is a Python-based software collection targeting computational cardiac electrophysiology problems. cbcbeat contains solvers of varying complexity and performance for the classical monodomain and bidomain equations coupled with cardiac cell models. The cbcbeat solvers are based on algorithms described in (Sundnes et al. 2006) and the core FEniCS Project software (Logg et al. 2012). All solvers allow for automated derivation and computation of adjoint and tangent linear solutions, functional derivatives and Hessians via the dolfin-adjoint software (Farrell et al. 2013). The computation of functional derivatives in turn allows for automated and efficient solution of optimization problems such as those encountered in data assimillation or other inverse problems.

Subfields: Cardiology and Cardiovascular Medicine, Electrical and Electronic Engineering, Neurology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Paris Perdikaris
2. F. Sahli Costabal
3. Daniel E. Hurtado
4. Ellen Kuhl
5. Yibo Yang

In Electrical and Electronic Engineering

1. Ellen Kuhl
2. Paris Perdikaris

In Neurology

1. Paris Perdikaris
2. Yibo Yang

Citations in the corpus

• Physics-Informed Neural Networks for Cardiac Activation Mapping (2020, methodology): “We use the open-source software cbcbeat [34] to perform the simulation.”