For as long as industry has run on heat—through turbines, reactors, and cooling towers—engineers have wrestled with the limits of prediction: formulas that hold only under familiar conditions, simulations that fracture when reality grows complex. Researchers Sadegh Ataee and Mehran Ameri of Shahid Bahonar University of Kerman have now charted a path beyond those limits, embedding the laws of thermodynamics directly into neural networks to create digital twins that reason not merely from data, but from physical truth. Their work, centered on a novel exergy-based loss function that honors both c