A Machine Learning Surrogate to Enhance Local Conduction Velocity Prediction in Cardiac Models

Research topic/area

Computational Cardiac Modeling

Type of thesis

Master

Start time

21.05.2026

Application deadline

31.12.2026

Duration of the thesis

6 months

Description

Motivation
Computer models of cardiac electrical wave propagation help to understand and treat heart rhythm disorders. The conduction velocity (CV) of these waves depends on local conditions such as the tissue recovery state, wavefront shape, fiber direction, and neighboring cell states.

The monodomain model captures all of these dependencies through its underlying physics, but is computation- ally expensive. The DREAM (Diffusion Reaction Eikonal Alternant Model) is significantly faster, but to achieve this, it needs the local CV as an explicit input rather than computing it from first principles. Currently, DREAM predicts CV using an analytic formula that only considers the local recovery state. Spatial factors, such as wavefront curvature, neighbor recovery, fiber alignment, and tissue heterogeneity are ignored, even though they can significantly affect propagation.

An accurate CV prediction that accounts for both, temporal and spatial context, could substantially improve DREAM's fidelity. Ideally, without sacrificing its speed advantage. This project explores whether a data-driven model, trained on detailed reference simulations, can learn such a prediction.

Student Project
In this project we will use machine learning to predict conduction velocity from local spatiotemporal features. The work covers the full pipeline: setting up and running monodomain reference simulations to generate ground-truth data, extracting local features and ground-truth conduction velocities from the simulation output, developing and training a prediction model, as well as optimizing its architecture and hyperparameters. Once a suitable model is found, it will be integrated into the DREAM solver within our C++ codebase, replacing the current analytic CV formula. Finally the new approach will be validated on unseen scenarios, comparing accuracy and runtime against both the original DREAM and full monodomain simulations.

More details can be found on the webpage:
https://www.ibt.kit.edu/english/6373.php

Requirement

Requirements for students

• Python and C++ programming skills are beneficial.
• The thesis can be conducted in English or German.

Faculty departments

• Engineering sciences
  Informatics
  Biomedical Engineering
  Electrical Engineering and Information Technology
• Natural sciences and Technology
  Mathematics

Supervision

Title, first name, last name

Stephanie Appel

Organizational unit

Institute of Biomedical Engineering (IBT)

Email address

stephanie.appel@kit.edu

Link to personal homepage/personal page

Website

Application via email

Application documents

• Curriculum vitae
• Grade transcript

E-Mail Address for application
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an stephanie.appel@kit.edu


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