Biophysically detailed mathematical modeling of cardiac electrophysiology is often computationally demanding, for example, when solving problems for various patient pathological conditions. Furthermore, it is still difficult to reduce the discrepancy between the output of idealised mathematical models and clinical measurements, which are usually noisy.In this work, we propose a fast physics-based deep learning framework to learn complex cardiac electrophysiology dynamics from data. This novel framework has two components, decomposing the dynamics into a physical term and a data-driven term, respectively. This construction allows the framework to learn from data of different complexity. Using in silico data, we demonstrate that this framework can reproduce the complex dynamics of transmembrane potential, even in presence of noise in the data. This combined physics-based data-driven approach may improve cardiac electrophysiology modeling by providing a robust biophysical tool for predictions.