HeartSimSage: Attention-Enhanced Graph Neural Networks for Accelerating Cardiac Mechanics Modeling.

Lei Shi, Yurui Chen, Vijay Vedula

Abstract read

In one paragraph

Article in Journal of computational physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Cardiac mechanics modeling: recent developments and current challenges.ArXiv · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lei ShiDepartment of Mechanical Engineering, Kennesaw State University, Marietta, GA, 30060, USA.

Yurui ChenDepartment of Mechanical Engineering, Columbia University, New York, NY, 10027, USA.

Vijay VedulaDepartment of Mechanical Engineering, Columbia University, New York, NY, 10027, USA.

Funding

Integrating Generative AI with Biomechanics Modeling to Advance Ventricular Disease Diagnosis and Personalized TreatmentR15HL181637 · NHLBI · KENNESAW STATE UNIVERSITY · PI SHI, LEI · 2025 to 2025

$523k

NHLBI NIH HHS R15 HL181637

6 · The paper itself

Abstract

Finite element analysis (FEA) is a powerful tool that forms the cornerstone of modeling cardiac biomechanics. However, FEA is computationally expensive for creating digital twins, which typically involves performing tens or hundreds of FEA simulations to estimate tissue parameters, limiting its clinical application. We have developed an attention-enhanced graph neural network (GNN)-based FEA emulator,

Indexed as

attention mechanismbiventricular cardiac mechanicscardiac mechanics emulatorfeature engineeringfinite element analysisgraph neural networks

Identifiers

PMID42125631

DOI10.1016/j.jcp.2026.114895

PMCPMC13160281

What OpenQuestion holds

Textmetadata

LicenceTDM

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.