Daniela Calvetti

187 papers receiving 3.9k citations

Peers

Daniela Calvetti
Comparison fields: 5 of 148
  • Computational Mathematics 74
  • Numerical Analysis 517
  • Mathematical Physics 845
  • Computational Theory and Mathematics 1.1k
  • Applied Mathematics 503
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Tryphon T. Georgiou United States
Jack X. Xin United States
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Citations per year

Countries citing papers authored by Daniela Calvetti

Since Specialization
Citations

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

Fields of papers citing papers by Daniela Calvetti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniela Calvetti, 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 Daniela Calvetti Line = papers co-authored together Daniela Calvetti links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 197 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2000405
2 1996168
3 2007153
4 2003128
5 2018109
6 1998103
7 1999101
8 200098
9
An implicitly restarted Lanczos method for large symmetric eigenvalue problems
199486
10 200880
11 200280
12 200377
13 200066
14 200961
15 200259
16
On the Choice of Subspace for Iterative Methods for Linear Discrete Ill-Posed Problems
200157
17 200457
18 200753
19 200451
20 200351

About Daniela Calvetti

Daniela Calvetti is a scholar working on Computational Theory and Mathematics, Mathematical Physics, Computational Mechanics, Numerical Analysis and Applied Mathematics, having authored 197 papers that have together received 4.2k indexed citations. Recurring topics across this work include Matrix Theory and Algorithms (47 papers), Numerical methods in inverse problems (39 papers), Sparse and Compressive Sensing Techniques (30 papers), Electromagnetic Scattering and Analysis (24 papers), Image and Signal Denoising Methods (21 papers), Statistical and numerical algorithms (18 papers), Neuroscience and Neuropharmacology Research (13 papers) and Electrical and Bioimpedance Tomography (12 papers). The work is most often cited by research in Computational Mathematics (74 citations), Numerical Analysis (517 citations), Mathematical Physics (845 citations), Computational Theory and Mathematics (1.1k citations) and Applied Mathematics (503 citations). Daniela Calvetti has collaborated with scholars based in United States, Italy and Finland. Frequent co-authors include Lothar Reichel, Erkki Somersalo, Fiorella Sgallari, Bryan W. Lewis, Serena Morigi, Gene Howard Golub, James Baglama, Rossana Occhipinti, William B. Gragg and Jari P. Kaipio. Their work appears in journals such as Inverse Problems, Journal of Computational and Applied Mathematics, Journal of Theoretical Biology, Numerical Algorithms and Linear Algebra and its Applications.

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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