  Widespread adoption of high-temperature polymer electrolyte membrane fuel
cells (HT-PEMFCs) and HT-PEM electrochemical hydrogen pumps (HT-PEM ECHPs)
requires models and computational tools that provide accurate scale-up and
optimization. Knowledge-based modeling has limitations as it is time consuming
and requires information about the system that is not always available (e.g.,
material properties and interfacial behavior between different materials).
Data-driven modeling on the other hand, is easier to implement, but often
necessitates large datasets that could be difficult to obtain. In this
contribution, knowledge-based modeling and data-driven modeling are uniquely
combined by implementing a Few-Shot Learning (FSL) approach. A knowledge-based
model originally developed for a HT-PEMFC was used to generate simulated data
(887,735 points) and used to pretrain a neural network source model.
Furthermore, the source model developed for HT-PEMFCs was successfully applied
to HT-PEM ECHPs - a different electrochemical system that utilizes similar
materials to the fuel cell. Experimental datasets from both HT-PEMFCs and
HT-PEM ECHPs with different materials and operating conditions (~50 points
each) were used to train 8 target models via FSL. Models for the unseen data
reached high accuracies in all cases (rRMSE between 1.04 and 3.73% for HT-PEMCs
and between 6.38 and 8.46% for HT-PEM ECHPs).
