Machine Learning-Based Surrogate Models and Transfer Learning for Derivative Free Optimization of HTPEM Fuel Cells

Authors

Luis A. Briceno-Mena, Cain Department of Chemical Engineering
Christopher G. Arges, Penn State College of Engineering
Jose A. Romagnoli, Cain Department of Chemical Engineering

Document Type

Article

Publication Date

1-1-2022

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.

Publication Source (Journal or Book title)

Computer Aided Chemical Engineering

First Page

1537

Last Page

1542

Recommended Citation

Briceno-Mena, L., Arges, C., & Romagnoli, J. (2022). Machine Learning-Based Surrogate Models and Transfer Learning for Derivative Free Optimization of HTPEM Fuel Cells. Computer Aided Chemical Engineering, 51, 1537-1542. https://doi.org/10.1016/B978-0-323-95879-0.50257-5