HeartUnloadNet: A cycle-consistent graph network with reduced supervision for predicting unloaded cardiac geometry from diastolic states

HeartUnloadNet provides a scalable and accurate alternative to traditional inverse finite element approaches for estimating the unloaded cardiac geometry. By combining mesh-aware learning with physiological conditioning and reduced supervision, the framework achieves real-time performance while maintaining biomechanical fidelity. This work establishes a foundation for future integration of learning-based surrogates into clinical workflows, supporting patient-specific cardiac modeling and real-time functional assessment.