# HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology

arXiv: https://arxiv.org/abs/2403.15433 · HTML: https://mlsanity.com/paper/2403.15433/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 4 of 4)

- [Universal Differential Equations for Scientific Machine Learning](https://mlsanity.com/paper/2001.04385/) (2020)
- [NeuralSim: Augmenting Differentiable Simulators with Neural Networks](https://mlsanity.com/paper/2011.04217/) (2020)
- [Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-Learning](https://mlsanity.com/paper/2210.02967/) (2022)
- [Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction](https://mlsanity.com/paper/2007.09522/) (2020)

## Built on by (most cited first, 2 of 2)

- [Learning functional components of PDEs from data using neural networks](https://mlsanity.com/paper/2602.13174/) (2026)
- [Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure](https://mlsanity.com/paper/2606.18154/) (2026)

## Abstract (arXiv)

Personalized virtual heart models have demonstrated increasing potential for clinical use, although the estimation of their parameters given patient-specific data remain a challenge. Traditional physics-based modeling approaches are computationally costly and often neglect the inherent structural errors in these models due to model simplifications and assumptions. Modern deep learning approaches, on the other hand, rely heavily on data supervision and lacks interpretability. In this paper, we present a novel hybrid modeling framework to describe a personalized cardiac digital twin as a combination of a physics-based known expression augmented by neural network modeling of its unknown gap to reality. We then present a novel meta-learning framework to enable the separate identification of both the physics-based and neural components in the hybrid model. We demonstrate the feasibility and generality of this hybrid modeling framework with two examples of instantiations and their proof-of-concept in synthetic experiments.