Thermodynamics-informed Graph Neural Networks for anatomically accurate digital human twins.

Abstract

Digital twins have emerged as a way to simulate human physiology, aiming to reduce the need for costly and ethically challenging clinical trials. However, their complexity currently limits the scope of what can be simulated. Hybrid neural networks present a promising and transparent alternative, guiding AI-based methodologies away from opaque, black-box models.