Physics-Informed Neural Network for modeling and predicting temperature fluctuations in proton exchange membrane electrolysis

Proton Exchange Membrane (PEM) electrolysis stands as a cornerstone technology in the clean energy sector, driving the production of hydrogen and oxygen from water. A critical aspect of ensuring the efficiency and safety of this process lies in the precise monitoring and control of temperature at the electrolysis outlet. However, accurately characterizing temperature changes within the PEM electrolysis system can be challenging due to the fluctuation of renewable energies. This study introduces an approach integrating data with fundamental physics principles known as Physics-Informed Neural Networks (PINNs). This method solves differential equations and estimates the unknown parameters governing the…