Physics-Informed Neural Operators for Cardiac Electrophysiology

Hannah Lydon, Milad Kazemi, Martin Bishop, Nicola Paoletti
Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR 331:1322-1338, 2026.

Abstract

Accurately simulating systems governed by PDEs, such as voltage fields in cardiac electrophysiology modelling, remains a modelling challenge. Traditional numerical solvers are computationally expensive and sensitive to discretisation whilst data driven machine learning inspired methods tend to be limited by the availability of training data and struggle with chaotic dynamics and long term predictions. Physics-Informed Machine Learning (PIML) approaches, such as Physics-Informed Neural Networks (PINNs), incorporate domain knowledge via physical constraints, but still remain limited by mesh resolution and long-term predictive stability. In this work, we propose a Physics-Informed Neural Operator (PINO) approach to this PDE simulation problem, as they are not limited to the resolution of the training mesh and learn over function spaces as opposed to single PDE instances, making them ideal for generalizable models. Our PINO model is able to accurately simulate electrophysiology dynamics over long time horizons and across multiple propagation scenarios, make predictions in a recursive fashion, and can scale its predictive resolution by up to 10x the training resolution whilst drastically reducing the simulation time at inference, highlighting its potential for efficient and scalable cardiac electrophysiology simulations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v331-lydon26a, title = {Physics-Informed Neural Operators for Cardiac Electrophysiology}, author = {Lydon, Hannah and Kazemi, Milad and Bishop, Martin and Paoletti, Nicola}, booktitle = {Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, pages = {1322--1338}, year = {2026}, editor = {Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, volume = {331}, series = {Proceedings of Machine Learning Research}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v331/main/assets/lydon26a/lydon26a.pdf}, url = {https://proceedings.mlr.press/v331/lydon26a.html}, abstract = {Accurately simulating systems governed by PDEs, such as voltage fields in cardiac electrophysiology modelling, remains a modelling challenge. Traditional numerical solvers are computationally expensive and sensitive to discretisation whilst data driven machine learning inspired methods tend to be limited by the availability of training data and struggle with chaotic dynamics and long term predictions. Physics-Informed Machine Learning (PIML) approaches, such as Physics-Informed Neural Networks (PINNs), incorporate domain knowledge via physical constraints, but still remain limited by mesh resolution and long-term predictive stability. In this work, we propose a Physics-Informed Neural Operator (PINO) approach to this PDE simulation problem, as they are not limited to the resolution of the training mesh and learn over function spaces as opposed to single PDE instances, making them ideal for generalizable models. Our PINO model is able to accurately simulate electrophysiology dynamics over long time horizons and across multiple propagation scenarios, make predictions in a recursive fashion, and can scale its predictive resolution by up to 10x the training resolution whilst drastically reducing the simulation time at inference, highlighting its potential for efficient and scalable cardiac electrophysiology simulations.} }
Endnote
%0 Conference Paper %T Physics-Informed Neural Operators for Cardiac Electrophysiology %A Hannah Lydon %A Milad Kazemi %A Martin Bishop %A Nicola Paoletti %B Proceedings of The 8th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2026 %E Gaurav Sukhatme %E Lars Lindemann %E Stephen Tu %E Adam Wierman %E Nikolay Atanasov %F pmlr-v331-lydon26a %I PMLR %P 1322--1338 %U https://proceedings.mlr.press/v331/lydon26a.html %V 331 %X Accurately simulating systems governed by PDEs, such as voltage fields in cardiac electrophysiology modelling, remains a modelling challenge. Traditional numerical solvers are computationally expensive and sensitive to discretisation whilst data driven machine learning inspired methods tend to be limited by the availability of training data and struggle with chaotic dynamics and long term predictions. Physics-Informed Machine Learning (PIML) approaches, such as Physics-Informed Neural Networks (PINNs), incorporate domain knowledge via physical constraints, but still remain limited by mesh resolution and long-term predictive stability. In this work, we propose a Physics-Informed Neural Operator (PINO) approach to this PDE simulation problem, as they are not limited to the resolution of the training mesh and learn over function spaces as opposed to single PDE instances, making them ideal for generalizable models. Our PINO model is able to accurately simulate electrophysiology dynamics over long time horizons and across multiple propagation scenarios, make predictions in a recursive fashion, and can scale its predictive resolution by up to 10x the training resolution whilst drastically reducing the simulation time at inference, highlighting its potential for efficient and scalable cardiac electrophysiology simulations.
APA
Lydon, H., Kazemi, M., Bishop, M. & Paoletti, N.. (2026). Physics-Informed Neural Operators for Cardiac Electrophysiology. Proceedings of The 8th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 331:1322-1338 Available from https://proceedings.mlr.press/v331/lydon26a.html.

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