Arun L. Gain

13 papers receiving 785 citations

Hit Papers

On the Virtual Element Method for three-dimensional linea...2014202620182022201450100150200250

Peers

Arun L. Gain
Comparison fields: 5 of 45
  • Mechanics of Materials 535
  • Civil and Structural Engineering 422
  • Computational Mechanics 365
  • Computational Theory and Mathematics 292
  • Electrical and Electronic Engineering 109
Replace S. M. Tavakkoli with:
S. M. Tavakkoli Iran
Heng Chi United States
Allan Gersborg-Hansen Denmark
Guodong Zhang United States
Allan Roulund Gersborg Denmark
Albert A. Saputra Australia
Yulin Mei China
Christian Hesch Germany
Johannes Linhard Germany
Martin Heinstein United States
Arun L. Gain relative to S. M. Tavakkoli Iran S. M. Tavakkoli's profile →
Citations per field
00.5×10×15×21.8×
S. M. Tavakkoli · 1×
Citations per year

Countries citing papers authored by Arun L. Gain

Since Specialization
Citations

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

Fields of papers citing papers by Arun L. Gain

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arun L. Gain. 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 Arun L. Gain. The network helps show where Arun L. Gain may publish in the future.

Co-authorship network of co-authors of Arun L. Gain

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

All Works

13 of 13 papers shown
#WorkIndexed citations
1 42
2 47
3 67
4 39
5 107
6 28
7 66
8
Polytope-based topology optimization using a mimetic-inspired method
2
9
On the Virtual Element Method for three-dimensional linear elasticity problems on arbitrary polyhedral meshesbreakdown →
285
10 42
11 45
12 33
13
A hybrid technique to extract cohesive fracture properties of elasto-plastic materials using inverse analysis and digital image correlation
2

About Arun L. Gain

Arun L. Gain is a scholar working on Architecture, Computational Theory and Mathematics and Civil and Structural Engineering, having authored 13 papers that have together received 805 indexed citations. Recurring topics across this work include Topology Optimization in Engineering (10 papers), Advanced Multi-Objective Optimization Algorithms (6 papers) and Composite Structure Analysis and Optimization (4 papers). The work is most often cited by research in Mechanics of Materials (535 citations), Civil and Structural Engineering (422 citations) and Computational Theory and Mathematics (292 citations). Arun L. Gain has collaborated with scholars based in United States and Brazil. Frequent co-authors include Gláucio H. Paulino, Cameron Talischi, Julián A. Norato, Shanglong Zhang, Ivan F. M. Menezes, Chau H. Le, John Lambros and Jay Carroll. Their work appears in journals such as Computer Methods in Applied Mechanics and Engineering, International Journal for Numerical Methods in Engineering and International Journal of Fracture.

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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