HAL (France)
open access
Self-loop physics-informed neural network for model predictive control of PEM electrolyzers
Effective thermal management of proton exchange membrane (PEM) electrolyzers is critical for ensuring both high efficiency and long-term durability. In this work, a physics-informed neural network (PINN) surrogate model is developed to accurately and efficiently predict the thermal behavior of a PEM
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