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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Combustion machine learning: Principles, progress and prospects
Matthias Ihme, Wai Tong Chung, Aashwin Mishra
Progress in Energy and Combustion Science (2022) Vol. 91, pp. 101010-101010
Open Access | Times Cited: 164

Showing 1-25 of 164 citing articles:

Combustion, Chemistry, and Carbon Neutrality
Katharina Kohse‐Höinghaus
Chemical Reviews (2023) Vol. 123, Iss. 8, pp. 5139-5219
Open Access | Times Cited: 118
A Review of Physics-Informed Machine Learning in Fluid Mechanics
Pushan Sharma, Wai Tong Chung, Bassem Akoush, et al.
Energies (2023) Vol. 16, Iss. 5, pp. 2343-2343
Open Access | Times Cited: 89
Improving aircraft performance using machine learning: A review
Soledad Le Clainche, Esteban Ferrer, S. Gibson, et al.
Aerospace Science and Technology (2023) Vol. 138, pp. 108354-108354
Open Access | Times Cited: 80
A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data
Mustafa Z. Yousif, Linqi Yu, Sergio Hoyas, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 64
The transition to sustainable combustion: Hydrogen- and carbon-based future fuels and methods for dealing with their challenges
Heinz Pitsch
Proceedings of the Combustion Institute (2024) Vol. 40, Iss. 1-4, pp. 105638-105638
Open Access | Times Cited: 16
FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flames
Jiahao Wu, Su Zhang, Yuxin Wu, et al.
Combustion and Flame (2025) Vol. 273, pp. 113964-113964
Closed Access | Times Cited: 2
Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions
Florian Stadtmann, Adil Rasheed, Trond Kvamsdal, et al.
IEEE Access (2023) Vol. 11, pp. 110762-110795
Open Access | Times Cited: 29
Data-driven models and digital twins for sustainable combustion technologies
Alessandro Parente, N. Swaminathan
iScience (2024) Vol. 27, Iss. 4, pp. 109349-109349
Open Access | Times Cited: 9
Data-driven prediction of laminar burning velocity for ternary ammonia/hydrogen/methane/air premixed flames
Cihat Emre Üstün, Sven Eckart, Agustín Valera-Medina, et al.
Fuel (2024) Vol. 368, pp. 131581-131581
Open Access | Times Cited: 8
Machine Learning-Based Molecular Dynamics Studies on Predicting Thermophysical Properties of Ethanol–Octane Blends
Amirali Shateri, Zhiyin Yang, Jianfei Xie
Energy & Fuels (2025)
Closed Access | Times Cited: 1
End-gas autoignition and detonation in confined space
Lei Zhou, Xiaojun Zhang, Kai Luo, et al.
Progress in Energy and Combustion Science (2025) Vol. 108, pp. 101217-101217
Closed Access | Times Cited: 1
Filtered Density Function
Hua Zhou, Peyman Givi, Zhuyin Ren
Cambridge University Press eBooks (2025), pp. 127-150
Closed Access | Times Cited: 1
Data-driven optimization of turbulent kinetic energy and tumble-y in combustion engines: A comparative study of machine learning models
Amirali Shateri, Zhiyin Yang, Yun Liu, et al.
Fuel (2025) Vol. 389, pp. 134590-134590
Open Access | Times Cited: 1
Extrapolation Performance of Convolutional Neural Network-Based Combustion Models for Large-Eddy Simulation: Influence of Reynolds Number, Filter Kernel and Filter Size
Geveen Arumapperuma, Nicola Sorace, Mark J. Jansen, et al.
Flow Turbulence and Combustion (2025)
Open Access | Times Cited: 1
Multiscale Physics-Informed Neural Networks for Stiff Chemical Kinetics
Yuting Weng, Dezhi Zhou
The Journal of Physical Chemistry A (2022) Vol. 126, Iss. 45, pp. 8534-8543
Closed Access | Times Cited: 34
Utilizing Artificial intelligence to identify an Optimal Machine learning model for predicting fuel consumption in Diesel engines
Amirali Shateri, Zhiyin Yang, Jianfei Xie
Energy and AI (2024) Vol. 16, pp. 100360-100360
Open Access | Times Cited: 8
Machine learning based technique for outlier detection and result prediction in combustion diagnostics
Mingfei Chen, Kaile Zhou, Dong Liu
Energy (2024) Vol. 290, pp. 130218-130218
Closed Access | Times Cited: 7
Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview
André Nicolle, Sili Deng, Matthias Ihme, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 3, pp. 597-620
Closed Access | Times Cited: 7
Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows
Ludovico Nista, Christoph Schumann, Temistocle Grenga, et al.
Proceedings of the Combustion Institute (2022) Vol. 39, Iss. 4, pp. 5279-5288
Open Access | Times Cited: 23
Neural network approach to response surface development for reaction model optimization and uncertainty minimization
Yue Zhang, Wendi Dong, Laurien A. Vandewalle, et al.
Combustion and Flame (2023) Vol. 251, pp. 112679-112679
Open Access | Times Cited: 16
Kinetics parameter optimization of hydrocarbon fuels via neural ordinary differential equations
Xingyu Su, Weiqi Ji, Jian An, et al.
Combustion and Flame (2023) Vol. 251, pp. 112732-112732
Closed Access | Times Cited: 16
The reactor-based perspective on finite-rate chemistry in turbulent reacting flows: A review from traditional to low-emission combustion
Arthur Péquin, Michael J. Evans, Alfonso Chinnici, et al.
Applications in Energy and Combustion Science (2023) Vol. 16, pp. 100201-100201
Open Access | Times Cited: 16
Artificial Intelligence-Machine Learning Algorithms for the Simulation of Combustion Thermal Analysis
Arunim Bhattacharya, Pradip Majumdar
Heat Transfer Engineering (2023) Vol. 45, Iss. 2, pp. 176-193
Closed Access | Times Cited: 13
Applying machine learning techniques to predict laminar burning velocity for ammonia/hydrogen/air mixtures
Cihat Emre Üstün, Mohammad Reza Herfatmanesh, Agustín Valera-Medina, et al.
Energy and AI (2023) Vol. 13, pp. 100270-100270
Open Access | Times Cited: 13
Graphics processing unit/artificial neural network-accelerated large-eddy simulation of swirling premixed flames
Min Zhang, Runze Mao, Han Li, et al.
Physics of Fluids (2024) Vol. 36, Iss. 5
Closed Access | Times Cited: 5
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