Physics-Informed Neural Operators for Cardiac Electrophysiology

TL;DR

This paper introduces a Physics-Informed Neural Operator (PINO) method for cardiac electrophysiology PDE modeling, enabling scalable, accurate, and long-term predictions across multiple scenarios with reduced computational costs.

Contribution

The work presents a novel PINO approach that generalizes PDE solutions across mesh resolutions and initial conditions, outperforming traditional PINNs in stability and scalability.

Findings

PINO models accurately reproduce cardiac EP dynamics over long time horizons.

Models generalize to unseen scenarios without retraining.

Simulation time is significantly reduced compared to numerical PDE solvers.

Abstract

Accurately simulating systems governed by PDEs, such as voltage fields in cardiac electrophysiology (EP) modelling, remains a significant modelling challenge. Traditional numerical solvers are computationally expensive and sensitive to discretisation, while canonical deep learning methods are data-hungry and struggle with chaotic dynamics and long-term predictions. Physics-Informed Neural Networks (PINNs) mitigate some of these issues by incorporating physical constraints in the learning process, yet they remain limited by mesh resolution and long-term predictive stability. In this work, we propose a Physics-Informed Neural Operator (PINO) approach to solve PDE problems in cardiac EP. Unlike PINNs, PINO models learn mappings between function spaces, allowing them to generalise to multiple mesh resolutions and initial conditions. Our results show that PINO models can accurately reproduce cardiac EP dynamics over extended time horizons and across multiple propagation scenarios, including zero-shot evaluations on scenarios unseen during training. Additionally, our PINO models maintain high predictive quality in long roll-outs (where predictions are recursively fed back as inputs), and can scale their predictive resolution by up to 10x the training resolution. These advantages come with a significant reduction in simulation time compared to numerical PDE solvers, highlighting the potential of PINO-based approaches for efficient and scalable cardiac EP simulations.