M. Muraskin

426 citations
64 papers · 378 · h-index 10

Impact in

Papers in

M. Muraskin

60 papers receiving 370 citations

Peers

M. Muraskin
Comparison fields: 5 of 38
  • Statistical and Nonlinear Physics 186
  • Computer Graphics and Computer-Aided Design 50
  • Nuclear and High Energy Physics 68
  • Numerical Analysis 29
  • Cognitive Neuroscience 93
Replace Ivan Dynnikov with:
Ivan Dynnikov Russia
Mario Casartelli Italy
Andrei Ludu United States
Giuseppe Genovese Switzerland
Takaomi Shigehara Japan
Yan Gu China
N. J. Papastamatiou United States
Mitsuhiro Nishida Japan
Michel Vittot France
Vojkan Jakšić Canada
M. Muraskin relative to Ivan Dynnikov Russia Ivan Dynnikov's profile →
Citations per field
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Ivan Dynnikov · 1×
Citations per year

Countries citing papers authored by M. Muraskin

Since Specialization
Citations

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

Fields of papers citing papers by M. Muraskin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 3 scholars most cited alongside M. Muraskin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with M. Muraskin Line = papers co-authored together M. Muraskin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 64 papers — load more, or switch the sort, to bring in the rest.

#Work
1 196348
2 196125
3 197521
4 197020
5 197115
6 197513
7 197010
8 19739
9 19729
10 19899
11 19718
12 19888
13 19728
14 19898
15 19898
16 19888
17 19738
18
Aesthetic fields without integrability
19857
19 19777
20 19807

About M. Muraskin

M. Muraskin is a scholar working on Statistical and Nonlinear Physics, Atomic and Molecular Physics, and Optics, Cognitive Neuroscience, Condensed Matter Physics and Computer Vision and Pattern Recognition, having authored 64 papers that have together received 378 indexed citations. Recurring topics across this work include Advanced Mathematical Theories and Applications (15 papers), Aesthetic Perception and Analysis (13 papers), Quantum Mechanics and Applications (10 papers), Theoretical and Computational Physics (10 papers), Quantum chaos and dynamical systems (8 papers), Computer Graphics and Visualization Techniques (8 papers), Nonlinear Waves and Solitons (6 papers) and Data Visualization and Analytics (5 papers). The work is most often cited by research in Statistical and Nonlinear Physics (186 citations), Computer Graphics and Computer-Aided Design (50 citations), Nuclear and High Energy Physics (68 citations), Numerical Analysis (29 citations) and Cognitive Neuroscience (93 citations). M. Muraskin has collaborated with scholars based in United States. Frequent co-authors include Sheldon L. Glashow, K. Nishijima and Tyler Clark. Their work appears in journals such as Mathematical and Computer Modelling, Applied Mathematics and Computation, Computers & Mathematics with Applications, Annals of Physics and International Journal of Theoretical Physics.

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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