## 1 Introduction
For example, as a result of the participation in the Cardiac Electrophysiological Simulation Challenge (CESC'10) MICCAI-STACOM workshop ([[https://stacom.github.io](https://stacom.github.io)]([https://stacom.github.io](https://stacom.github.io)) (accessed on 26 April 2022)), several research groups [22] developed a pipeline integrating different modeling approaches to predict depolarization isochrones from optical mapping data of a perfused ex vivo porcine heart with different pacing conditions [23], acquired at the Sunnybrook Health Sciences Centre, Toronto, Canada. However, experimental data were available for two cases. Some years later, [PERSON] et al. [24] developed a swine model of LBBB to study the link between electrical and mechanical dyssynchrony, and their correction with CRT. The authors generated a unique dataset with signal, multi-modal images and electro-anatomical maps at different stages of the disease in tens of infarcted and non-infarcted animals. [PERSON] et al. [25] proposed advanced visualization techniques and metrics to quantify the differences in electrical activation patterns at baseline, LBBB and CRT stages. A subset of the database was the foundation for the organization of the CRT-EPiggy19 challenge ([[https://crt-epiggy19.surge.sh/](https://crt-epiggy19.surge.sh/)]([https://crt-epiggy19.surge.sh/](https://crt-epiggy19.surge.sh/)) (accessed on 15 April 2022)) at the MICCAI-STACOM19 workshop,which is available open access in a public repository ([[https://zenodo.org/record/3249511](https://zenodo.org/record/3249511)]([https://zenodo.org/record/3249511](https://zenodo.org/record/3249511)) (accessed on 18 April 2022)). More recently, [PERSON] et al. [PERSON] et al. (2019) also developed a swine model of the heart that was coupled with electrophysiological models to study advanced biomaterial injection therapies for ischemic heart failure. Participants at the CRT-EPiggy19 challenge adopted different modeling approaches to predict the electrical activation after CRT. [PERSON] et al. [PERSON] et al. (2019) and [PERSON] and [PERSON] and [PERSON] (2019) developed personalization strategies based on genetic algorithms to estimate regional conduction velocities with simple but fast phenomenological Eikonal-based models. Meanwhile, [PERSON] and [PERSON] (2019) used a more detailed Ten Tusscher-Panfilov [PERSON] (2020) for cellular electrophysiology, considering transmural heterogeneity and electrical propagation by a monodomain model. After the challenge, other researchers have used the provided data to better understand cardiac physiology and pacing-based therapies [PERSON] et al. (2020). All the aforementioned approaches are based on solving the electrophysiological model equations with the finite-element method (FEM) as a numerical technique based on a mesh discretization of the biventricular heart geometry, as it is the common choice in cardiac modeling [PERSON] et al. (2019). In FEM, the computational domain is divided into discrete subsets of interconnected nodes as elements. However, the explicit connectivity required in the domain leads to great difficulty in generating the irregular patient-specific cardiac meshes, which then becomes a tedious, manual, highly interactive, and time-consuming process. Moreover, the reliability of the simulation results is highly dependent on the quality of the built geometrical mesh [PERSON] et al. (2019). Additionally, mesh distortion that can occur during large cardiac deformations enforces the use of remeshing algorithms to restore mesh shape and numerical accuracy, thereby increasing the computational cost and efforts [PERSON] et al. (2019). Meshless methods are an interesting alternative to avoid meshing difficulties, since the spatial domain is composed of an unstructured particle cloud without connectivity. Therefore, the meshless domain construction procedure can be used for any type of complex geometry.