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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:

Differentiable hybrid neural modeling for fluid-structure interaction
Xiantao Fan, Jianxun Wang
Journal of Computational Physics (2023) Vol. 496, pp. 112584-112584
Open Access | Times Cited: 19

Showing 19 citing articles:

A novel framework for predicting active flow control by combining deep reinforcement learning and masked deep neural network
Yangwei Liu, Feitong Wang, Shihang Zhao, et al.
Physics of Fluids (2024) Vol. 36, Iss. 3
Open Access | Times Cited: 19
Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation
Han Gao, Xu Han, Xiantao Fan, et al.
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 427, pp. 117023-117023
Open Access | Times Cited: 8
Differentiable turbulence: Closure as a partial differential equation constrained optimization
Varun Shankar, Dibyajyoti Chakraborty, Venkatasubramanian Viswanathan, et al.
Physical Review Fluids (2025) Vol. 10, Iss. 2
Closed Access | Times Cited: 1
A novel hybrid framework for efficient higher order ODE solvers using neural networks and block methods
V. Murugesh, M. Priyadharshini, Yogesh Kumar Sharma, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Generative learning for forecasting the dynamics of high-dimensional complex systems
Han Gao, Sebastian Kaltenbach, Petros Koumoutsakos
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 4
Neural differentiable modeling with diffusion-based super-resolution for two-dimensional spatiotemporal turbulence
Xiantao Fan, Deepak Akhare, Jianxun Wang
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 433, pp. 117478-117478
Open Access | Times Cited: 4
Spatio-temporal transfer learning for multiphase flow prediction in the fluidized bed reactor
Xinyu Xie, Yichen Hao, Pu Zhao, et al.
Applied Thermal Engineering (2025), pp. 126247-126247
Closed Access
Gradient-free optimization of non-differentiable hybrid neural solvers for spatially heterogeneous composites
Hanfeng Zhang, Tengfei Luo, Jianxun Wang
Theoretical and Applied Mechanics Letters (2025), pp. 100585-100585
Open Access
Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process
Deepak Akhare, Zeping Chen, Richard Gulotty, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 3
Computational fluid–structure interaction in biology and soft robots: A review
R. Pramanik, Roel Verstappen, Patrick R. Onck
Physics of Fluids (2024) Vol. 36, Iss. 10
Closed Access | Times Cited: 2
Conditional neural field latent diffusion model for generating spatiotemporal turbulence
Pan Du, Meet Hemant Parikh, Xiantao Fan, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 2
Neural Differentiable Modeling with Diffusion-Based Super-Resolution for Two-Dimensional Spatiotemporal Turbulence
Xiantao Fan, Deepak Akhare, Jianxun Wang
(2024)
Open Access | Times Cited: 1
Fluid–structure interaction in engineering based on its applications: A plenary and up-to-date review
Muhammad Idrees Afridi, Mohsen Izadi, Mohammad Eizadi, et al.
Physics of Fluids (2024) Vol. 36, Iss. 10
Closed Access | Times Cited: 1
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
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
Deciphering unknown upstream conditions and downstream turbulent flow evolution via neural networks
Reza Nouri, Ahmad Vasel‐Be‐Hagh
Ocean Engineering (2024) Vol. 297, pp. 116977-116977
Closed Access
An immersed boundary method using online sequential data assimilation
Miguel M. Valero, M. Meldi
Journal of Computational Physics (2024), pp. 113697-113697
Open Access
Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process
Jianxun Wang, Deepak Akhare, Zeping Chen, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 1
A Differentiable Programming Framework for Spin Models
Tiago de Souza Farias, Vitor V. Schultz, José C. M. Mombach, et al.
(2023)
Open Access
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