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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:

Cost function for low-dimensional manifold topology assessment
Kamila Zdybał, E. F. Armstrong, James C. Sutherland, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 14

Showing 14 citing articles:

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: 79
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: 8
Reduced-order modeling of supersonic fuel–air mixing in a multi-strut injection scramjet engine using machine learning techniques
Ali Can İspir, Kamila Zdybał, Bayindir H. Saracoglu, et al.
Acta Astronautica (2022) Vol. 202, pp. 564-584
Open Access | Times Cited: 26
A methodology for estimating hypersonic engine performance by coupling supersonic reactive flow simulations with machine learning techniques
Ali Can İspir, Bayindir H. Saracoglu, Thierry Magin, et al.
Aerospace Science and Technology (2023) Vol. 140, pp. 108501-108501
Closed Access | Times Cited: 13
Optimizing progress variables for ammonia/hydrogen combustion using encoding–decoding networks
Kamila Zdybał, James C. Sutherland, Alessandro Parente
Combustion and Flame (2025) Vol. 276, pp. 114152-114152
Open Access
Machine Learning and Its Application to Reacting Flows
N. Swaminathan, Alessandro Parente
Lecture notes in energy (2023)
Open Access | Times Cited: 10
Low-dimensional representation of intermittent geophysical turbulence with high-order statistics-informed neural networks (H-SiNN)
Raffaello Foldes, Enrico Camporeale, R. Marino
Physics of Fluids (2024) Vol. 36, Iss. 2
Open Access | Times Cited: 3
Local manifold learning and its link to domain-based physics knowledge
Kamila Zdybał, Giuseppe D’Alessio, Antonio Attili, et al.
Applications in Energy and Combustion Science (2023) Vol. 14, pp. 100131-100131
Open Access | Times Cited: 6
PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization
Kamila Zdybał, E. F. Armstrong, Alessandro Parente, et al.
SoftwareX (2023) Vol. 23, pp. 101447-101447
Open Access | Times Cited: 5
A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks
Dibyajyoti Nayak, Anirudh Jonnalagadda, Uma Balakrishnan, et al.
Combustion and Flame (2023) Vol. 259, pp. 113192-113192
Open Access | Times Cited: 4
Improving reduced-order models through nonlinear decoding of projection-dependent outputs
Kamila Zdybał, Alessandro Parente, James C. Sutherland
Patterns (2023) Vol. 4, Iss. 11, pp. 100859-100859
Open Access | Times Cited: 2
Reduced-order modeling with reconstruction-informed projections
E. F. Armstrong, James C. Sutherland
Combustion and Flame (2023) Vol. 259, pp. 113119-113119
Open Access | Times Cited: 1
Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches
Kamila Zdybał, Mohammad Rafi Malik, Axel Coussement, et al.
Lecture notes in energy (2023), pp. 245-278
Open Access
Reduced order design space analysis of for ramjet engines with data mining techniques
Bora O. Cakir, Ali Can İspir, Francesco Civerra, et al.
AIAA SCITECH 2022 Forum (2023)
Closed Access
Low-dimensional representation of intermittent geophysical turbulence with High-Order Statistics-informed Neural Networks (H-SiNN)
Raffaello Foldes, Enrico Camporeale, R. Marino
arXiv (Cornell University) (2023)
Open Access
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