We will combine biophysical simulation and deep learning methods with a longitudinal digital twin approach to optimise risk prediction and choice of therapy for atrial fibrillation. We aim to move predictions from the acute response to the long-term response; from the average patient to an individual patient; from standard treatments to any treatment approach; from small patient cohorts to large virtual trials; and from long simulation times to short clinical timescales.