Deep learning-based reduced order models in cardiac electrophysiology


The numerical simulation of multiple scenarios for cardiac electrophysiology (EP) problems easily becomes computationally prohibitive if relying on usual high-fidelity, full order models. To perform the numerical approximation of cardiac EP equations in multi-query contexts or solving them in real-time, we introduce a new generation of non-intrusive, nonlinear reduced order models, based on deep learning (DL) algorithms, such as convolutional, feedforward, and autoencoder neural networks. Numerical results show that the resulting DL-ROM technique allows to accurately capture in real-time complex fronts propagation processes on realistic geometries, both in physiological and pathological scenarios.

7th International Conference on Computational and Mathematical Biomedical Engineering
Stefania Fresca
Stefania Fresca
Assistant Professor

My research interests are scientific machine learning, reduced order modeling and AI.