Probabilistic machine learning framework for chemical source term integration with Gaussian Processes: H<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg" display="inline" id="d1e713"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>/air auto-ignition case-Reference-Cited by-同舟云学术

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Probabilistic machine learning framework for chemical source term integration with Gaussian Processes: H2/air auto-ignition case

Published:2024-09 Issue: Volume:81 Page:47-55
ISSN:0360-3199
Container-title:International Journal of Hydrogen Energy
language:en
Short-container-title:International Journal of Hydrogen Energy

Author:

Üstün Cihat Emre^ORCID,Paykani Amin^ORCID

Publisher

Elsevier BV

Reference39 articles.

1. Recent progress in the use of hydrogen as a fuel for internal combustion engines;Verhelst;Int J Hydrog Energy,2014

2. Recent advances in understanding of flammability characteristics of hydrogen;Sánchez;Prog Energy Combust Sci,2014

3. Hydrogen-fueled gas turbines in future energy system;Zhou;Int J Hydrog Energy,2024

4. Hydrogen-fuelled internal combustion engines-Bibliometric analysis on research trends, hotspots, and challenges;Abubakar;Int J Hydrog Energy,2024

5. The properties of hydrogen as fuel tomorrow in sustainable energy system for a cleaner planet;Momirlan;Int J Hydrog Energy,2005

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