Teeratorn Kadeethum

769 total citations
49 papers, 516 citations indexed

About

Teeratorn Kadeethum is a scholar working on Ocean Engineering, Statistical and Nonlinear Physics and Mechanics of Materials. According to data from OpenAlex, Teeratorn Kadeethum has authored 49 papers receiving a total of 516 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Ocean Engineering, 18 papers in Statistical and Nonlinear Physics and 18 papers in Mechanics of Materials. Recurrent topics in Teeratorn Kadeethum's work include Model Reduction and Neural Networks (18 papers), Hydraulic Fracturing and Reservoir Analysis (16 papers) and Drilling and Well Engineering (12 papers). Teeratorn Kadeethum is often cited by papers focused on Model Reduction and Neural Networks (18 papers), Hydraulic Fracturing and Reservoir Analysis (16 papers) and Drilling and Well Engineering (12 papers). Teeratorn Kadeethum collaborates with scholars based in United States, Denmark and Australia. Teeratorn Kadeethum's co-authors include Hamidreza M. Nick, Nikolaos Bouklas, Francesco Ballarin, Thomas Martini Jørgensen, Saeed Salimzadeh, Hongkyu Yoon, Youngsoo Choi, Daniel O’Malley, Sanghyun Lee and Hari Viswanathan and has published in prestigious journals such as PLoS ONE, Scientific Reports and Water Resources Research.

In The Last Decade

Teeratorn Kadeethum

42 papers receiving 506 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Teeratorn Kadeethum United States 14 237 176 157 143 142 49 516
Junsheng Zeng China 13 91 0.4× 283 1.6× 264 1.7× 84 0.6× 318 2.2× 34 615
Pu Ren United States 8 177 0.7× 86 0.5× 49 0.3× 36 0.3× 27 0.2× 13 393
Zhicheng Wang United States 10 285 1.2× 431 2.4× 52 0.3× 32 0.2× 31 0.2× 13 649
Ben Moseley United Kingdom 7 152 0.6× 68 0.4× 35 0.2× 27 0.2× 43 0.3× 13 361
Andrea Mola Italy 12 260 1.1× 287 1.6× 48 0.3× 24 0.2× 52 0.4× 36 471
George Em Karniadakis United States 6 219 0.9× 126 0.7× 44 0.3× 22 0.2× 17 0.1× 14 369
Maria Vasilyeva Russia 20 49 0.2× 789 4.5× 153 1.0× 804 5.6× 114 0.8× 93 1.1k
P. W. Sharp New Zealand 11 30 0.1× 118 0.7× 228 1.5× 159 1.1× 122 0.9× 34 643
Xupeng He Saudi Arabia 14 20 0.1× 51 0.3× 337 2.1× 150 1.0× 279 2.0× 77 595

Countries citing papers authored by Teeratorn Kadeethum

Since Specialization
Citations

This map shows the geographic impact of Teeratorn Kadeethum's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Teeratorn Kadeethum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Teeratorn Kadeethum more than expected).

Fields of papers citing papers by Teeratorn Kadeethum

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Teeratorn Kadeethum. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Teeratorn Kadeethum. The network helps show where Teeratorn Kadeethum may publish in the future.

Co-authorship network of co-authors of Teeratorn Kadeethum

This figure shows the co-authorship network connecting the top 25 collaborators of Teeratorn Kadeethum. A scholar is included among the top collaborators of Teeratorn Kadeethum based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Teeratorn Kadeethum. Teeratorn Kadeethum is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Salimzadeh, Saeed, et al.. (2025). Inferring fracture dilation and shear slip from surface deformation utilising trained surrogate models. International Journal of Rock Mechanics and Mining Sciences. 188. 106077–106077. 2 indexed citations
2.
Kadeethum, Teeratorn, Stephen Verzi, & Hongkyu Yoon. (2024). An improved neural operator framework for large-scale CO2 storage operations. Geoenergy Science and Engineering. 240. 213007–213007. 3 indexed citations
3.
4.
5.
Cheung, Siu Wun, Youngsoo Choi, H. Keo Springer, & Teeratorn Kadeethum. (2024). Data-scarce surrogate modeling of shock-induced pore collapse process. Shock Waves. 34(3). 237–256. 2 indexed citations
6.
7.
Fuhg, Jan N., Aditya P. Karmarkar, Teeratorn Kadeethum, Hongkyu Yoon, & Nikolaos Bouklas. (2023). Deep convolutional Ritz method: parametric PDE surrogates without labeled data. Applied Mathematics and Mechanics. 44(7). 1151–1174. 14 indexed citations
8.
Kadeethum, Teeratorn, John Jakeman, Youngsoo Choi, Nikolaos Bouklas, & Hongkyu Yoon. (2023). Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems. IEEE Access. 11. 62970–62985. 2 indexed citations
9.
Kadeethum, Teeratorn, et al.. (2023). Continuous conditional generative adversarial networks for data-driven modelling of geologic CO 2 storage and plume evolution. Gas Science and Engineering. 115. 204982–204982. 14 indexed citations
10.
Kadeethum, Teeratorn & Hongkyu Yoon. (2022). Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 3 indexed citations
11.
Kadeethum, Teeratorn & Hongkyu Yoon. (2022). Estimation of Mechanical Properties of Mancos Shale using Machine Learning Methods.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1 indexed citations
12.
Kadeethum, Teeratorn, Daniel O’Malley, Francesco Ballarin, et al.. (2022). Enhancing high-fidelity nonlinear solver with reduced order model. Scientific Reports. 12(1). 20229–20229. 10 indexed citations
13.
Kadeethum, Teeratorn, et al.. (2022). A framework for upscaling and modelling fluid flow for discrete fractures using conditional generative adversarial networks. Advances in Water Resources. 166. 104264–104264. 10 indexed citations
14.
Kadeethum, Teeratorn, Francesco Ballarin, Daniel O’Malley, et al.. (2022). Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning. Scientific Reports. 12(1). 20654–20654. 13 indexed citations
15.
Kadeethum, Teeratorn, Francesco Ballarin, & Nikolaos Bouklas. (2021). Data-driven reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation. GEM - International Journal on Geomathematics. 12(1). 13 indexed citations
16.
Kadeethum, Teeratorn, Thomas Martini Jørgensen, & Hamidreza M. Nick. (2020). Physics-informed neural networks for solving nonlinear diffusivity and Biot’s equations. PLoS ONE. 15(5). e0232683–e0232683. 89 indexed citations
17.
Salimzadeh, Saeed, et al.. (2019). The effect of stress distribution on the shape and direction of hydraulic fractures in layered media. Engineering Fracture Mechanics. 215. 151–163. 33 indexed citations
18.
Kadeethum, Teeratorn, Saeed Salimzadeh, & Hamidreza M. Nick. (2018). Investigation on the Productivity Behaviour in Deformable Heterogeneous Fractured Reservoirs. 5 indexed citations
19.
Kadeethum, Teeratorn, et al.. (2017). Uncertainty Analysis of Smart Waterflood Recovery Performance in Clastic Reservoirs. 14(1). 18–32. 4 indexed citations
20.
Kadeethum, Teeratorn, et al.. (2017). Uncertainties - Extension of Smart Waterflooding from Core to Field Scale. Proceedings. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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