{"ID":2856624,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12011","arxiv_id":"2510.12011","title":"TorchCor: High-Performance Cardiac Electrophysiology Simulations with the Finite Element Method on GPUs","abstract":"Cardiac electrophysiology (CEP) simulations are increasingly used for understanding cardiac arrhythmias and guiding clinical decisions. However, these simulations typically require high-performance computing resources with numerous CPU cores, which are often inaccessible to many research groups and clinicians. To address this, we present TorchCor, a high-performance Python library for CEP simulations using the finite element method on general-purpose GPUs. Built on PyTorch, TorchCor significantly accelerates CEP simulations, particularly for large 3D meshes. The accuracy of the solver is verified against manufactured analytical solutions and the $N$-version benchmark problem. TorchCor is freely available for both academic and commercial use without restrictions.","short_abstract":"Cardiac electrophysiology (CEP) simulations are increasingly used for understanding cardiac arrhythmias and guiding clinical decisions. However, these simulations typically require high-performance computing resources with numerous CPU cores, which are often inaccessible to many research groups and clinicians. To addre...","url_abs":"https://arxiv.org/abs/2510.12011","url_pdf":"https://arxiv.org/pdf/2510.12011v1","authors":"[\"Bei Zhou\",\"Maximilian Balmus\",\"Cesare Corrado\",\"Ludovica Cicci\",\"Shuang Qian\",\"Steven A. Niederer\"]","published":"2025-10-13T23:19:05Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[]","has_code":false}