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:
A hybrid partitioned deep learning methodology for moving interface and fluid–structure interaction
Rachit Gupta, Rajeev K. Jaiman
Computers & Fluids (2021) Vol. 233, pp. 105239-105239
Open Access | Times Cited: 33
Rachit Gupta, Rajeev K. Jaiman
Computers & Fluids (2021) Vol. 233, pp. 105239-105239
Open Access | Times Cited: 33
Showing 26-50 of 33 citing articles:
A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations
Rui Gao, Indu Kant Deo, Rajeev K. Jaiman
(2023)
Open Access | Times Cited: 3
Rui Gao, Indu Kant Deo, Rajeev K. Jaiman
(2023)
Open Access | Times Cited: 3
A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations
Rui Gao, Indu Kant Deo, Rajeev K. Jaiman
arXiv (Cornell University) (2022)
Open Access | Times Cited: 3
Rui Gao, Indu Kant Deo, Rajeev K. Jaiman
arXiv (Cornell University) (2022)
Open Access | Times Cited: 3
Numerical Study on the Energy Harvesting Performance of a Flapping Foil with Attached Flaps
Shihui Wu, Li Wang
Processes (2024) Vol. 12, Iss. 9, pp. 1963-1963
Open Access
Shihui Wu, Li Wang
Processes (2024) Vol. 12, Iss. 9, pp. 1963-1963
Open Access
More accurate representation of interaction at the fluid–structure interface with an improved smoothed field gradient method
Xiaofeng Wang, Yang‐Yu Liu, Siu-seong Law, et al.
Physics of Fluids (2024) Vol. 36, Iss. 12
Closed Access
Xiaofeng Wang, Yang‐Yu Liu, Siu-seong Law, et al.
Physics of Fluids (2024) Vol. 36, Iss. 12
Closed Access
Deep learning model for two-fluid flows
George El Haber, Jonathan Viquerat, Aurélien Larcher, et al.
Physics of Fluids (2023) Vol. 35, Iss. 2
Closed Access | Times Cited: 1
George El Haber, Jonathan Viquerat, Aurélien Larcher, et al.
Physics of Fluids (2023) Vol. 35, Iss. 2
Closed Access | Times Cited: 1
An autoencoder-based deep-learning method for augmenting the sensing capability of piezoelectric MEMS sensors in a fluid-dynamic system
Mohammadrahim Kazemzadeh, Iman Mehdipour, Massimo De Vittorio, et al.
(2023)
Open Access
Mohammadrahim Kazemzadeh, Iman Mehdipour, Massimo De Vittorio, et al.
(2023)
Open Access
An autoencoder-based deep-learning method for augmenting the sensing capability of piezoelectric MEMS sensors in a fluid-dynamic system
Mohammadrahim Kazemzadeh, Iman Mehdipour, Massimo De Vittorio, et al.
(2023)
Open Access
Mohammadrahim Kazemzadeh, Iman Mehdipour, Massimo De Vittorio, et al.
(2023)
Open Access
An Autoencoder‐Based Deep‐Learning Method for Augmenting the Sensing Capability of Piezoelectric Microelectromechanical System Sensors in a Fluid‐Dynamic System
Mohammadrahim Kazemzadeh, Iman Mehdipour, Massimo De Vittorio, et al.
Advanced Intelligent Systems (2023) Vol. 6, Iss. 3
Open Access
Mohammadrahim Kazemzadeh, Iman Mehdipour, Massimo De Vittorio, et al.
Advanced Intelligent Systems (2023) Vol. 6, Iss. 3
Open Access
