Advanced Nonlinear Studies

874 papers and 8.2k indexed citations i.

About

The 874 papers published in Advanced Nonlinear Studies in the last decades have received a total of 8.2k indexed citations. Papers published in Advanced Nonlinear Studies usually cover Applied Mathematics (689 papers), Computational Theory and Mathematics (460 papers) and Mathematical Physics (370 papers) specifically the topics of Nonlinear Partial Differential Equations (565 papers), Advanced Mathematical Modeling in Engineering (440 papers) and Advanced Mathematical Physics Problems (185 papers). The most active scholars publishing in Advanced Nonlinear Studies are Guozhen Lu, Dimitri Mugnai, Teresa D’Aprile, Claudianor O. Alves, Nikolaos S. Papageorgiou, Pietro d’Avenia, Vicenţiu D. Rădulescu, Xianhua Tang, Zhi-Qiang Wang and Herbert Amann.

In The Last Decade

Fields of papers published in Advanced Nonlinear Studies

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Advanced Nonlinear Studies. 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 Advanced Nonlinear Studies.

Countries where authors publish in Advanced Nonlinear Studies

Since Specialization
Citations

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