Nonlinear Analysis

12.9k papers and 200.7k indexed citations

About

The 12.9k papers published in Nonlinear Analysis in the last decades have received a total of 200.7k indexed citations. Papers published in Nonlinear Analysis usually cover Applied Mathematics (7.5k papers), Computational Theory and Mathematics (5.7k papers) and Mathematical Physics (3.8k papers) specifically the topics of Advanced Mathematical Modeling in Engineering (3.9k papers), Nonlinear Partial Differential Equations (3.5k papers) and Nonlinear Differential Equations Analysis (3.0k papers). The most active scholars publishing in Nonlinear Analysis are V. Lakshmikantham, Hong‐Kun Xu, Gary M. Lieberman, Xianling Fan, Emmanuele DiBenedetto, Juan J. Nieto, Wataru Takahashi, Naseer Shahzad, Pierre‐Louis Lions and Louis Jeanjean.

In The Last Decade

Nonlinear Analysis

12.0k papers receiving 180.7k citations

Fields of papers published in Nonlinear Analysis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Nonlinear 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 Nonlinear Analysis.

Countries where authors publish in Nonlinear Analysis

Since Specialization
Citations

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