Countries where authors publish in Inverse Problems
Since Specialization
Citations
This map shows the geographic impact of research published in Inverse Problems. 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 Inverse Problems with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Inverse Problems more than expected).
This network shows the impact of papers published in Inverse Problems. 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 Inverse Problems.
About Inverse Problems
The 4.4k papers published in Inverse Problems in the last decades have received a total of 123.3k indexed citations . Papers published in Inverse Problems usually cover Mathematical Physics (2.5k papers), Applied Mathematics (492 papers), Computational Theory and Mathematics (783 papers), Geophysics (529 papers) and Statistical and Nonlinear Physics (456 papers) specifically the topics of Numerical methods in inverse problems (2.3k papers), Microwave Imaging and Scattering Analysis (913 papers), Advanced Mathematical Modeling in Engineering (605 papers), Sparse and Compressive Sensing Techniques (430 papers), Geophysical Methods and Applications (396 papers), Electrical and Bioimpedance Tomography (387 papers), Ultrasonics and Acoustic Wave Propagation (353 papers) and Medical Imaging Techniques and Applications (342 papers). The most active scholars publishing in Inverse Problems are Simon Arridge, Charles L. Byrne, Andreas Kirsch, Masahiro Yamamoto, DAVID L. COLTON, Hong‐Kun Xu, Liliana Borcea, Richard Bamler, Philipp Hartl and Emmanuel J. Candès.
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.