Yu. L. Daletskiǐ

487 citations
27 papers · 304 indexed · h-index 10
Topics
Advanced Mathematical Modeling in Engineering (5 papers)advanced mathematical theories (5 papers)Advanced Topics in Algebra (5 papers)
Partner nations
Russia

In The Last Decade

Yu. L. Daletskiǐ

20 papers receiving 208 citations

Peers

Yu. L. Daletskiǐ
Comparison fields: 5 of 42
  • Mathematical Physics 167
  • Applied Mathematics 133
  • Computational Theory and Mathematics 93
  • Finance 80
  • Statistical and Nonlinear Physics 43
Replace Klaus Bichteler with:
Klaus Bichteler United States
Raphael H�egh-Krohn Norway
Ichirō Shigekawa Japan
J. I. Horváth Hungary
Shizan Fang France
D. A. Storvick United States
W. Karwowski Poland
Shigeki Aida Japan
Simon J. A. Malham United Kingdom
Eugene Lytvynov United Kingdom
Yu. L. Daletskiǐ relative to Klaus Bichteler United States Klaus Bichteler's profile →
Citations per field
00.5×3.3×
Klaus Bichteler · 1×
Citations per year

Countries citing papers authored by Yu. L. Daletskiǐ

Since Specialization
Citations

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

Fields of papers citing papers by Yu. L. Daletskiǐ

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu. L. Daletskiǐ

This figure shows the co-authorship network connecting the top 25 collaborators of Yu. L. Daletskiǐ. A scholar is included among the top collaborators of Yu. L. Daletskiǐ 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 Yu. L. Daletskiǐ. Yu. L. Daletskiǐ 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
#WorkIndexed citations
1 0
2 0
3 1
4 7
5 9
6 10
7 2
8 3
9
Lie Superalgebras in a Hamiltonian Operator Theory
2
10 21
11 13
12 1
13 1
14 9
15 1
16 1
17 23
18 68
19 25
20 31

About Yu. L. Daletskiǐ

Yu. L. Daletskiǐ is a scholar working on Mathematical Physics, Algebra and Number Theory and Applied Mathematics, having authored 27 papers that have together received 304 indexed citations. Recurring topics across this work include Advanced Mathematical Modeling in Engineering (5 papers), advanced mathematical theories (5 papers) and Advanced Topics in Algebra (5 papers). The work is most often cited by research in Mathematical Physics (167 citations), Applied Mathematics (133 citations) and Finance (80 citations). Yu. L. Daletskiǐ has collaborated with scholars based in Russia. Frequent co-authors include Ya. I. Belopolskaya, Sergey Fomin, Boris Tsygan, Israel M. Gelfand, O. G. Smolyanov, М. М. Лаврентьев, Yu. M. Berezanskiĭ, Vladimir I. Arnold, Yu. G. Reshetnyak and Э. М. Семенов. Their work appears in journals such as Lecture notes in mathematics, Russian Mathematical Surveys and Functional Analysis and Its Applications.

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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