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A Numerical Study of Scalable Cardiac Electro-Mechanical Solvers on HPC Architectures

Overview of attention for article published in Frontiers in Physiology, April 2018
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Title
A Numerical Study of Scalable Cardiac Electro-Mechanical Solvers on HPC Architectures
Published in
Frontiers in Physiology, April 2018
DOI 10.3389/fphys.2018.00268
Pubmed ID
Authors

Piero Colli Franzone, Luca F. Pavarino, Simone Scacchi

Abstract

We introduce and study some scalable domain decomposition preconditioners for cardiac electro-mechanical 3D simulations on parallel HPC (High Performance Computing) architectures. The electro-mechanical model of the cardiac tissue is composed of four coupled sub-models: (1) the static finite elasticity equations for the transversely isotropic deformation of the cardiac tissue; (2) the active tension model describing the dynamics of the intracellular calcium, cross-bridge binding and myofilament tension; (3) the anisotropic Bidomain model describing the evolution of the intra- and extra-cellular potentials in the deforming cardiac tissue; and (4) the ionic membrane model describing the dynamics of ionic currents, gating variables, ionic concentrations and stretch-activated channels. This strongly coupled electro-mechanical model is discretized in time with a splitting semi-implicit technique and in space with isoparametric finite elements. The resulting scalable parallel solver is based on Multilevel Additive Schwarz preconditioners for the solution of the Bidomain system and on BDDC preconditioned Newton-Krylov solvers for the non-linear finite elasticity system. The results of several 3D parallel simulations show the scalability of both linear and non-linear solvers and their application to the study of both physiological excitation-contraction cardiac dynamics and re-entrant waves in the presence of different mechano-electrical feedbacks.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 27%
Researcher 3 12%
Professor > Associate Professor 2 8%
Student > Master 2 8%
Professor 1 4%
Other 3 12%
Unknown 8 31%
Readers by discipline Count As %
Engineering 6 23%
Mathematics 4 15%
Computer Science 2 8%
Environmental Science 1 4%
Psychology 1 4%
Other 1 4%
Unknown 11 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 April 2018.
All research outputs
#20,481,952
of 23,043,346 outputs
Outputs from Frontiers in Physiology
#9,494
of 13,785 outputs
Outputs of similar age
#291,042
of 329,683 outputs
Outputs of similar age from Frontiers in Physiology
#317
of 436 outputs
Altmetric has tracked 23,043,346 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,785 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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