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.

• →PR-Net embeds analytical transport equations as a computational backbone, reducing training variance by 39-fold compared to pure neural networks.
• →The model maintains over 97% accuracy when extrapolating to extreme conditions 2.5 times beyond training data.
• →Traditional AI approaches fail catastrophically in extrapolation scenarios, with standard neural networks dropping to 58.7% accuracy.
• →The system autonomously captures physical phenomena like membrane swelling without explicit programming.
• →Millisecond-level inference enables real-time monitoring for industrial hydrogen production safety systems.