Comparing Classical Ordinary Differential Equation and Neural Network Models for Reduced-Order Single-Cell Electrophysiology - Werneck, Yan Barbosa; Martins, Bernardo; Weber, Rodrigo (Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2025)
Electrophysiology modeling is key for non-invasive diagnostics and understanding heart and brain function. Traditional models use ODEs, from detailed ion channel dynamics to reduced-order phenomenological models. We compare a fast reduced-order model with data-driven and physics-informed neural networks as efficient alternatives to numerical solutions. Using the FitzHugh-Nagumo model, we trained networks with numerical data and model physics, employing architecture optimization, adaptive point density, and time-domain splitting. Inference via TensorRT achieved up to 1.8× speedup over optimized CUDA solvers with minimal accuracy loss. These results showcase neural networks as viable emulators when complexity is controlled.