Yeonjong Shin

637 total citations
20 papers, 342 citations indexed

About

Yeonjong Shin is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Statistics, Probability and Uncertainty. According to data from OpenAlex, Yeonjong Shin has authored 20 papers receiving a total of 342 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 10 papers in Statistical and Nonlinear Physics and 6 papers in Statistics, Probability and Uncertainty. Recurrent topics in Yeonjong Shin's work include Model Reduction and Neural Networks (10 papers), Neural Networks and Applications (7 papers) and Probabilistic and Robust Engineering Design (6 papers). Yeonjong Shin is often cited by papers focused on Model Reduction and Neural Networks (10 papers), Neural Networks and Applications (7 papers) and Probabilistic and Robust Engineering Design (6 papers). Yeonjong Shin collaborates with scholars based in United States, South Korea and Bangladesh. Yeonjong Shin's co-authors include Dongbin Xiu, George Em Karniadakis, Zhongqiang Zhang, Jérôme Darbon, Zhen Zhang, Liang Yan, Lu Lu, Mark Ainsworth, Sanghyun Lee and Kailiang Wu and has published in prestigious journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and Neural Networks.

In The Last Decade

Yeonjong Shin

20 papers receiving 324 citations

Peers

Yeonjong Shin

SNP

SPU

CSE

Yiping Lu China

Kookjin Lee United States

Tong Qin United States

Elizabeth Qian United States

Alessandro Alla Italy

Qifeng Liao China

J. Nathan Kutz United States

Susmita Mall India

Kadierdan Kaheman United States

Ido Bright United States

Yiping Lu China

Yeonjong Shin

260 ×1.5

SNP

93 ×0.9

101 ×1.0

67 ×0.8

SPU

31 ×0.6

CSE

Citations per year, relative to Yeonjong Shin Yeonjong Shin (= 1×) peers Yiping Lu

Countries citing papers authored by Yeonjong Shin

Since Specialization

Citations

This map shows the geographic impact of Yeonjong Shin'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 Yeonjong Shin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yeonjong Shin more than expected).

Fields of papers citing papers by Yeonjong Shin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yeonjong Shin. 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 Yeonjong Shin. The network helps show where Yeonjong Shin may publish in the future.

Co-authorship network of co-authors of Yeonjong Shin

This figure shows the co-authorship network connecting the top 25 collaborators of Yeonjong Shin. A scholar is included among the top collaborators of Yeonjong Shin 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 Yeonjong Shin. Yeonjong Shin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Cheung, Siu Wun, et al.. (2024). tLaSDI: Thermodynamics-informed latent space dynamics identification. Computer Methods in Applied Mechanics and Engineering. 429. 117144–117144. 2 indexed citations

Cheung, Siu Wun, et al.. (2024). S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models. SIAM Journal on Scientific Computing. 46(4). B474–B501. 5 indexed citations

Lee, Sanghyun & Yeonjong Shin. (2024). On the Training and Generalization of Deep Operator Networks. SIAM Journal on Scientific Computing. 46(4). C273–C296. 14 indexed citations

He, Xiaolong, Siu Wun Cheung, Yeonjong Shin, et al.. (2024). A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling. arXiv (Cornell University). 6 indexed citations

Shin, Yeonjong, Jérôme Darbon, & George Em Karniadakis. (2023). Accelerating gradient descent and Adam via fractional gradients. Neural Networks. 161. 185–201. 16 indexed citations

Shin, Yeonjong, Zhongqiang Zhang, & George Em Karniadakis. (2023). ERROR ESTIMATES OF RESIDUAL MINIMIZATION USING NEURAL NETWORKS FOR LINEAR PDES. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 4(4). 73–101. 28 indexed citations

Zhang, Zhen, Yeonjong Shin, & George Em Karniadakis. (2022). GFINNs: GENERIC formalism informed neural networks for deterministic and stochastic dynamical systems. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 380(2229). 20210207–20210207. 40 indexed citations

Shin, Yeonjong, et al.. (2022). Approximation rates of DeepONets for learning operators arising from advection–diffusion equations. Neural Networks. 153. 411–426. 51 indexed citations

Ainsworth, Mark & Yeonjong Shin. (2022). Active Neuron Least Squares: A Training Method for Multivariate Rectified Neural Networks. SIAM Journal on Scientific Computing. 44(4). A2253–A2275. 4 indexed citations

10.

Ainsworth, Mark & Yeonjong Shin. (2021). Plateau Phenomenon in Gradient Descent Training of RELU Networks: Explanation, Quantification, and Avoidance. SIAM Journal on Scientific Computing. 43(5). A3438–A3468. 14 indexed citations

11.

Shin, Yeonjong. (2021). Effects of depth, width, and initialization: A convergence analysis of layer-wise training for deep linear neural networks. Analysis and Applications. 20(1). 73–119. 3 indexed citations

12.

Shin, Yeonjong, Jérôme Darbon, & George Em Karniadakis. (2020). On the Convergence and generalization of Physics Informed Neural Networks.. arXiv (Cornell University). 33 indexed citations

13.

Shin, Yeonjong & George Em Karniadakis. (2020). TRAINABILITY OF ReLU NETWORKS AND DATA-DEPENDENT INITIALIZATION. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1(1). 39–74. 4 indexed citations

14.

Shin, Yeonjong, Kailiang Wu, & Dongbin Xiu. (2018). Sequential function approximation with noisy data. Journal of Computational Physics. 371. 363–381. 5 indexed citations

15.

Wu, Kailiang, Yeonjong Shin, & Dongbin Xiu. (2017). A Randomized Tensor Quadrature Method for High Dimensional Polynomial Approximation. SIAM Journal on Scientific Computing. 39(5). A1811–A1833. 7 indexed citations

16.

Yan, Liang, Yeonjong Shin, & Dongbin Xiu. (2017). Sparse Approximation using $\ell_1-\ell_2$ Minimization and Its Application to Stochastic Collocation. SIAM Journal on Scientific Computing. 39(1). A229–A254. 32 indexed citations

17.

Shin, Yeonjong & Dongbin Xiu. (2017). A Randomized Algorithm for Multivariate Function Approximation. SIAM Journal on Scientific Computing. 39(3). A983–A1002. 11 indexed citations

18.

Shin, Yeonjong & Dongbin Xiu. (2016). On a near optimal sampling strategy for least squares polynomial regression. Journal of Computational Physics. 326. 931–946. 19 indexed citations

19.

Shin, Yeonjong & Dongbin Xiu. (2016). Correcting Data Corruption Errors for Multivariate Function Approximation. SIAM Journal on Scientific Computing. 38(4). A2492–A2511. 7 indexed citations

20.

Shin, Yeonjong & Dongbin Xiu. (2016). Nonadaptive Quasi-Optimal Points Selection for Least Squares Linear Regression. SIAM Journal on Scientific Computing. 38(1). A385–A411. 41 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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