Erman Guleryuz

507 total citations · 1 hit paper
8 papers, 353 citations indexed

About

Erman Guleryuz is a scholar working on Statistical and Nonlinear Physics, Mechanics of Materials and Materials Chemistry. According to data from OpenAlex, Erman Guleryuz has authored 8 papers receiving a total of 353 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Statistical and Nonlinear Physics, 3 papers in Mechanics of Materials and 3 papers in Materials Chemistry. Recurrent topics in Erman Guleryuz's work include Model Reduction and Neural Networks (3 papers), Machine Learning in Materials Science (2 papers) and Advanced Numerical Methods in Computational Mathematics (2 papers). Erman Guleryuz is often cited by papers focused on Model Reduction and Neural Networks (3 papers), Machine Learning in Materials Science (2 papers) and Advanced Numerical Methods in Computational Mathematics (2 papers). Erman Guleryuz collaborates with scholars based in United States, United Kingdom and Bulgaria. Erman Guleryuz's co-authors include Seid Korić, Diab Abueidda, Nahil Sobh, Sinisa Dj. Mesarovic, Junyan He, Iwona Jasiuk, Xin Chen, Akash Singh, Yumeng Li and Cleve Ashcraft and has published in prestigious journals such as International Journal for Numerical Methods in Engineering, Materials & Design and Computers & Structures.

In The Last Decade

Erman Guleryuz

7 papers receiving 345 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep learning for topology optimization of 2D metamaterials

2020 253 citations Diab Abueidda, Seid Korić et al. Materials & Design profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Erman Guleryuz United States	5	142	118	97	82	61	8	353
Hang Yang China	11	127 0.9×	180 1.5×	106 1.1×	199 2.4×	83 1.4×	26	464
Vladimir Buljak Serbia	13	104 0.7×	185 1.6×	60 0.6×	181 2.2×	41 0.7×	24	383
Pierre Joyot France	9	69 0.5×	212 1.8×	123 1.3×	59 0.7×	38 0.6×	22	328
Abdelmalek Zine France	14	165 1.2×	110 0.9×	224 2.3×	190 2.3×	27 0.4×	57	573
Slaviša Šalinić Serbia	13	161 1.1×	128 1.1×	89 0.9×	125 1.5×	63 1.0×	51	469
Shirko Faroughi Iran	13	163 1.1×	121 1.0×	37 0.4×	176 2.1×	41 0.7×	40	406
Alexander Humer Austria	12	129 0.9×	87 0.7×	61 0.6×	182 2.2×	20 0.3×	39	371
Shujun Tan China	12	122 0.9×	115 1.0×	64 0.7×	50 0.6×	14 0.2×	41	399
M. Koishi Japan	11	173 1.2×	202 1.7×	114 1.2×	323 3.9×	65 1.1×	18	588
Anas Batou France	14	312 2.2×	142 1.2×	70 0.7×	92 1.1×	35 0.6×	41	479

Countries citing papers authored by Erman Guleryuz

Since Specialization

Citations

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

Fields of papers citing papers by Erman Guleryuz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Erman Guleryuz

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

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

He, Junyan, et al.. (2023). Improving the accuracy of the deep energy method. Acta Mechanica. 234(12). 5975–5998. 11 indexed citations

2.

Abueidda, Diab, Seid Korić, Erman Guleryuz, & Nahil Sobh. (2022). Enhanced physics‐informed neural networks for hyperelasticity. International Journal for Numerical Methods in Engineering. 124(7). 1585–1601. 50 indexed citations

3.

Singh, Akash, Xin Chen, Yumeng Li, Seid Korić, & Erman Guleryuz. (2020). Development of Artificial Neural Network Potential for Graphene. AIAA Scitech 2020 Forum. 3 indexed citations

4.

Abueidda, Diab, et al.. (2020). Deep learning for topology optimization of 2D metamaterials. Materials & Design. 196. 109098–109098. 253 indexed citations breakdown →

5.

Rouet, François-Henry, Cleve Ashcraft, Roger G. Grimes, et al.. (2020). Scalability Challenges of an Industrial Implicit Finite Element Code. 505–514. 1 indexed citations

6.

Ashcraft, Cleve, Roger G. Grimes, Erman Guleryuz, et al.. (2019). Running Jet Engine Models on Thousands of Processors with LS-DYNA Implicit.

7.

Guleryuz, Erman & Sinisa Dj. Mesarovic. (2016). Dislocation Nucleation on Grain Boundaries: Low Angle Twist and Asymmetric Tilt Boundaries. Crystals. 6(7). 77–77. 12 indexed citations

8.

Korić, Seid, et al.. (2014). Evaluation of massively parallel linear sparse solvers on unstructured finite element meshes. Computers & Structures. 141. 19–25. 23 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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