Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Machine Learning and Its Application to Reacting Flows
N. Swaminathan, Alessandro Parente
Lecture notes in energy (2023)
Open Access | Times Cited: 10

Showing 10 citing articles:

A Data-Driven Approach to Refine the Partially Stirred Reactor Closure for Turbulent Premixed Flames
Lorenzo Piu, Arthur Péquin, Rodolfo S. M. Freitas, et al.
Flow Turbulence and Combustion (2025)
Open Access
Data-driven multifidelity surrogate models for rocket engines injector design
José Felix Zapata Usandivaras, Michaël Bauerheim, Bénédicte Cuenot, et al.
Data-Centric Engineering (2025) Vol. 6
Open Access
Data-Driven Modeling for Coarse Graining
Richard D. Sandberg, Markus Klein
Cambridge University Press eBooks (2025), pp. 202-221
Closed Access
Paradigms and Tools

Cambridge University Press eBooks (2025), pp. 3-4
Closed Access
Segmentation of high-speed flow fields using physics-informed clustering
Michael Ullman, Shivam Barwey, Gyu‐Sub Lee, et al.
Applications in Energy and Combustion Science (2023) Vol. 15, pp. 100181-100181
Open Access | Times Cited: 5
Acceleration of complex high-performance computing ensemble simulations with super-resolution-based subfilter models
Mathis Bode, Jens Henrik Göbbert
Computers & Fluids (2023) Vol. 271, pp. 106150-106150
Closed Access | Times Cited: 5
Controlling thermoacoustic instability of a laminar premixed flame with deep reinforcement learning and neural autoregressive models
Juan Camilo Giraldo Delgado, Khalid Alhazmi, Inna Gorbatenko, et al.
Proceedings of the Combustion Institute (2024) Vol. 40, Iss. 1-4, pp. 105223-105223
Closed Access | Times Cited: 1
Characteristic time scale as optimal input in Machine Learning algorithms: Homogeneous autoignition
Mohammed I. Radaideh, Stelios Rigopoulos, Dimitris A. Goussis
Energy and AI (2023) Vol. 14, pp. 100273-100273
Open Access | Times Cited: 2
Towards a generalised artificial neural network for sub-grid filtered density function closure in turbulent combustion
Hanying Yang, Tota Kobayashi, Salvatore Iavarone, et al.
Applications in Energy and Combustion Science (2023) Vol. 14, pp. 100142-100142
Open Access | Times Cited: 2
Mechanical behaviour of fiber reinforced polymer concrete (FRPC)
Manikandaprabhu Sundaramoorthy, Mohamed Shaariq, D. Kathirvel, et al.
AIP conference proceedings (2024) Vol. 3187, pp. 020010-020010
Closed Access
Page 1

Scroll to top