TorchCor, a high-performance Python library built on PyTorch, significantly accelerates cardiac electrophysiology simulations on GPUs and is verified for accuracy against analytical solutions.
TorchCor provides an accessible, high-performance GPU-based Python library for cardiac electrophysiology simulations, potentially democratizing access to these computational tools for researchers and clinicians.
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.
Zhou et al. (Mon,) conducted a other in Cardiac arrhythmias (simulation). TorchCor (Python library for CEP simulations) vs. Analytical solutions and N-version benchmark was evaluated on Accuracy of the solver. TorchCor, a high-performance Python library built on PyTorch, significantly accelerates cardiac electrophysiology simulations on GPUs and is verified for accuracy against analytical solutions.
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