Achiya Dax

795 total citations
72 papers, 566 citations indexed

About

Achiya Dax is a scholar working on Computational Theory and Mathematics, Numerical Analysis and Computational Mechanics. According to data from OpenAlex, Achiya Dax has authored 72 papers receiving a total of 566 indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Computational Theory and Mathematics, 46 papers in Numerical Analysis and 20 papers in Computational Mechanics. Recurrent topics in Achiya Dax's work include Matrix Theory and Algorithms (44 papers), Advanced Optimization Algorithms Research (41 papers) and Sparse and Compressive Sensing Techniques (14 papers). Achiya Dax is often cited by papers focused on Matrix Theory and Algorithms (44 papers), Advanced Optimization Algorithms Research (41 papers) and Sparse and Compressive Sensing Techniques (14 papers). Achiya Dax collaborates with scholars based in Israel, Sweden and United Kingdom. Achiya Dax's co-authors include Shmuel Kaniel, Brian Berkowitz, Michael Zilberbrand, A. Melloul, Yehuda Bachmat, Lars Eldén and Ronit Nativ and has published in prestigious journals such as SHILAP Revista de lepidopterología, Water Resources Research and Journal of Hydrology.

In The Last Decade

Achiya Dax

66 papers receiving 485 citations

Peers

Achiya Dax
Comparison fields: 5 of 73
  • Computational Theory and Mathematics 286
  • Numerical Analysis 236
  • Computational Mechanics 130
  • Applied Mathematics 78
  • Mathematical Physics 58
Replace U. von Matt with:
U. von Matt United States
Aurél Galántai Hungary
Mårten Gulliksson Sweden
R. E. Funderlic United States
Constantin Udrişte Romania
Paola Favati Italy
Jinyan Fan China
George E. Trapp United States
Günther Nürnberger Germany
James S. Vandergraft United States
U. von Matt United States View profile →
Citations per field, relative to Achiya Dax
Achiya Dax · 1×
Citations per year, relative to Achiya Dax
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Countries citing papers authored by Achiya Dax

Since Specialization
Citations

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

Fields of papers citing papers by Achiya Dax

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Achiya Dax

This figure shows the co-authorship network connecting the top 25 collaborators of Achiya Dax. A scholar is included among the top collaborators of Achiya Dax 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 Achiya Dax. Achiya Dax 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
# Work Indexed citations
1 1
2 4
3 2
4 3
5 6
6 3
7 1
8 1
9 1
10 11
11 29
12 6
13 3
14 19
15 9
16 3
17 16
18 1
19 11
20 3

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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