Applicable Analysis

4.3k papers and 37.5k indexed citations
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About

The 4.3k papers published in Applicable Analysis in the last decades have received a total of 37.5k indexed citations. Papers published in Applicable Analysis usually cover Applied Mathematics (2.1k papers), Computational Theory and Mathematics (1.8k papers) and Mathematical Physics (1.6k papers) specifically the topics of Advanced Mathematical Modeling in Engineering (1.3k papers), Stability and Controllability of Differential Equations (787 papers) and Numerical methods in inverse problems (686 papers). The most active scholars publishing in Applicable Analysis are Donal O’Regan, Mikko Salo, R. M. Brown, V. Lakshmikantham, Masahiro Yamamoto, Ravi P. Agarwal, Giovanni Alessandrini, Chaitan P. Gupta, Ludwik Byszewski and Juan J. Trujillo.

In The Last Decade

Applicable Analysis

3.8k papers receiving 33.1k citations

Fields of papers published in Applicable Analysis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Applicable Analysis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Applicable Analysis.

Countries where authors publish in Applicable Analysis

Since Specialization
Citations

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

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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2026