Hard-constraint physics-residual networks enable robust extrapolation for hydrogen crossover prediction in PEM water electrolyzers

Researchers developed a hard-constraint physics-residual network (PR-Net) that significantly improves hydrogen crossover prediction in water electrolyzers for green hydrogen production. The AI model achieves 99.57% accuracy and maintains performance when extrapolating beyond training conditions, outperforming traditional neural networks and physics-informed networks.