On the Hybrid Approach for Cardiac Electrophysiology Modeling Utilizing Spectral Neural Operators
Abstract
Purpose. To investigate the applicability of a hybrid approach combining spectral neural operators with classical numerical methods for accelerated cardiac electrophysiology simulation in the bidomain formulation.
Materials and methods. The bidomain model is considered on rectangular anisotropic 3D domains with the ten Tusscher-Panfilov ionic model. A hybrid scheme is investigated: an autoregressive Fourier neural operator (AR-FNO) approximates the nonlinear parabolic evolution of the transmembrane potential, while the elliptic coupling equation is solved by the conjugate gradient method. Gradient-aware training is employed to improve wavefront reproduction accuracy.
Results. On test 3D anisotropic slabs with a 2 ms time step, a conduction velocity error of 3–6 % relative to the reference finite element solution was obtained. An ablation study of individual method components was performed. Limitations were identified: error accumulation during prolonged autoregressive rollout and accuracy dependence on time step size.
Conclusion. The principal feasibility of applying hybrid neural network architectures for computational electrophysiology problems on model domains is demonstrated. The applicability boundaries and directions for further research are identified.
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