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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:

Deep neural operators as accurate surrogates for shape optimization
Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 129, pp. 107615-107615
Closed Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
Nick McGreivy, Ammar Hakim
Nature Machine Intelligence (2024) Vol. 6, Iss. 10, pp. 1256-1269
Closed Access | Times Cited: 17
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks
Khemraj Shukla, Juan Diego Toscano, Zhicheng Wang, et al.
Computer Methods in Applied Mechanics and Engineering (2024) Vol. 431, pp. 117290-117290
Open Access | Times Cited: 14
Flow3DNet: A deep learning framework for efficient simulation of three-dimensional wing flow fields
Kuijun Zuo, Zhengyin Ye, Xianxu Yuan, et al.
Aerospace Science and Technology (2025), pp. 109991-109991
Closed Access | Times Cited: 1
A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construction
Xu Chen, Ba Trung Cao, Yong Yuan, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108156-108156
Open Access | Times Cited: 10
Rethinking materials simulations: Blending direct numerical simulations with neural operators
Vivek Oommen, Khemraj Shukla, Saaketh Desai, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 8
A complete state-space solution model for inviscid flow around airfoils based on physics-informed neural networks
Wenbo Cao, Jiahao Song, Weiwei Zhang
Journal of Computational Physics (2024) Vol. 516, pp. 113285-113285
Open Access | Times Cited: 6
Diffeomorphism neural operator for various domains and parameters of partial differential equations
Zhao Zhiwei, Changqing Liu, Yingguang Li, et al.
Communications Physics (2025) Vol. 8, Iss. 1
Open Access
A Least-Squares-Based Neural Network (LS-Net) for Solving Linear Parametric PDEs
Shima Baharlouei, Jamie M. Taylor, Carlos Uriarte, et al.
Computer Methods in Applied Mechanics and Engineering (2025) Vol. 437, pp. 117757-117757
Closed Access
Neural operator-based super-fidelity: A warm-start approach for accelerating steady-state simulations
Xu‐Hui Zhou, Jiequn Han, Muhammad I. Zafar, et al.
Journal of Computational Physics (2025), pp. 113871-113871
Closed Access
Digital twin model of a permanent magnet synchronous motor via a multiphysics-informed deep operator network
Seho Son, Hyunseung Lee, Dayeon Jeong, et al.
Mechanical Systems and Signal Processing (2025) Vol. 229, pp. 112561-112561
Closed Access
A comprehensive deep learning geometric shape optimization framework with field prediction surrogate and reinforcement learning
Hao Ma, Jianing Liu, Mai Ye, et al.
Physics of Fluids (2024) Vol. 36, Iss. 4
Closed Access | Times Cited: 4
Interpreting and generalizing deep learning in physics-based problems with functional linear models
Amirhossein Arzani, Lingxiao Yuan, Pania Newell, et al.
Engineering With Computers (2024)
Closed Access | Times Cited: 4
A Comprehensive and Fair Comparison between Mlp and Kan Representations for Differential Equations and Operator Networks
Khemraj Shukla, Juan Diego Toscano, Zhi-Cheng Wang, et al.
(2024)
Open Access | Times Cited: 4
A hybrid Decoder-DeepONet operator regression framework for unaligned observation data
Bo Chen, Chenyu Wang, Weipeng Li, et al.
Physics of Fluids (2024) Vol. 36, Iss. 2
Open Access | Times Cited: 3
A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models
Liyue Wang, Haochen Zhang, Cong Wang, et al.
Mathematics (2024) Vol. 12, Iss. 10, pp. 1417-1417
Open Access | Times Cited: 3
Physics-informed geometric operators to support surrogate, dimension reduction and generative models for engineering design
Shahroz Khan, Zahid Masood, Muhammad Usama, et al.
Advanced Engineering Informatics (2024) Vol. 63, pp. 102937-102937
Closed Access | Times Cited: 2
Grad–Shafranov equilibria via data-free physics informed neural networks
Byoungchan Jang, Alan A. Kaptanoglu, Rahul Gaur, et al.
Physics of Plasmas (2024) Vol. 31, Iss. 3
Open Access | Times Cited: 1
Fast Buffet-Onset Prediction and Optimization Method Based on Pretrained Flowfield Prediction Model
Yunjia Yang, Runze Li, Yufei Zhang, et al.
AIAA Journal (2024), pp. 1-17
Open Access | Times Cited: 1
An operator learning perspective on parameter-to-observable maps
Daniel Zhengyu Huang, Nicholas H. Nelsen, Margaret Trautner
Foundations of Data Science (2024) Vol. 7, Iss. 1, pp. 163-225
Open Access | Times Cited: 1
A review on shape optimization of hulls and airfoils leveraging Computational Fluid Dynamics Data-Driven Surrogate models
Jake M. Walker, Andrea Coraddu, Luca Oneto
Ocean Engineering (2024) Vol. 312, pp. 119263-119263
Open Access | Times Cited: 1
Deep operator network surrogate for phase-field modeling of metal grain growth during solidification
Danielle Ciesielski, Yulan Li, Shenyang Hu, et al.
Computational Materials Science (2024) Vol. 246, pp. 113417-113417
Closed Access | Times Cited: 1
A scalable framework for learning the geometry-dependent solution operators of partial differential equations
Minglang Yin, Nicolas Charon, Ryan Brody, et al.
Nature Computational Science (2024)
Open Access | Times Cited: 1
Artificial Intelligence for Simulation of Soot Distribution Inside Porous Filter Walls
M. Khýr, M. Plachá, T. Hlavatý, et al.
(2024), pp. 85-92
Open Access
Data-Driven Approach to Estimating Soot Distribution Inside Catalytic Filters in Automotive Exhaust Gas Aftertreatment
M. Khýr, M. Plachá, T. Hlavatý, et al.
Engineering Mechanics ... (2024), pp. 154-157
Open Access
Kolmogorov n-widths for multitask physics-informed machine learning (PIML) methods: Towards robust metrics
Michael Penwarden, Houman Owhadi, Robert Kirby
Neural Networks (2024) Vol. 180, pp. 106703-106703
Closed Access
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