The field of computational cardiology is moving towards the development of more sophisticated models that incorporate complex biophysical processes, such as mechano-calcium feedback and biomechanical constraints. These models aim to provide a more accurate representation of cardiac function and mechanics, enabling better understanding and prediction of cardiac behavior under various conditions. Noteworthy papers in this area include one that proposes an eikonal-based framework for incorporating mechano-calcium feedback into cardiac electromechanical models, and another that presents a learning-based image registration approach that assimilates biomechanical constraints to infer tissue-specific deformation patterns. These innovative approaches have the potential to significantly advance the field of computational cardiology and improve our understanding of cardiac function and mechanics.