@inbook{6c8804f08091479499d1de675c53ab2e,
title = "Machine Learning-Based Surrogate Models and Transfer Learning for Derivative Free Optimization of HTPEM Fuel Cells",
abstract = "Widespread adoption of high-temperature electrochemical systems such as polymer electrolyte membrane fuel cells (HT-PEMFCs) requires models and computational tools for accurate optimization and guiding new materials for enhancing fuel cell performance and durability. In this contribution, knowledge-based modelling and data-driven modelling are combined using Few-Shot Learning and implementing an Automated Machine Learning framework for the generation of Machine Learning-based surrogate models.",
author = "Briceno-Mena, \{Luis A.\} and Arges, \{Christopher G.\} and Romagnoli, \{Jose A.\}",
note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",
year = "2022",
month = jan,
doi = "10.1016/B978-0-323-95879-0.50257-5",
language = "English (US)",
series = "Computer Aided Chemical Engineering",
publisher = "Elsevier B.V.",
pages = "1537--1542",
booktitle = "Computer Aided Chemical Engineering",
}