Luisa D’Amore
Impact in
- Numerical Analysis top 10%
- Iterative Methods for Nonlinear Equations
- Modeling and Simulation top 10%
- Fractional Differential Equations Solutions
Papers in
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- Numerical methods in inverse problems 11
-
- Parallel Computing and Optimization Techniques 7
- Co-authors
- A. MurliGiuliano LaccettiRossella ArcucciLivia MarcellinoSalvatore CuomoArdelio GallettiDiego RomanoGiuseppe Scotti
- Journals
- Journal of Computational and Applied Mathematics (4 papers)ACM Transactions on Mathematical Software (3 papers)Inverse Problems (3 papers)Journal of Scientific Computing (2 papers)Parallel Computing (2 papers)
- Partner nations
- ItalyUnited StatesUnited Kingdom
In The Last Decade
Luisa D’Amore
49 papers receiving 452 citations
Peers
Comparison fields: 5 of 73
- Numerical Analysis 64
- Modeling and Simulation 39
- Hardware and Architecture 50
- Atmospheric Science 117
- Computational Theory and Mathematics 67
Countries citing papers authored by Luisa D’Amore
This map shows the geographic impact of Luisa D’Amore'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 Luisa D’Amore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luisa D’Amore more than expected).
Fields of papers citing papers by Luisa D’Amore
This network shows the impact of papers produced by Luisa D’Amore. 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 Luisa D’Amore. The network helps show where Luisa D’Amore may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Luisa D’Amore, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 0 | |
| 2 | 2015 | 12 | |
| 3 | 2014 | 22 | |
| 4 | 2014 | 19 | |
| 5 | Data Assimilation Achievements in HPC sysyems: experiments on OceanVar in the Mediterranean Sea | 2013 | 0 |
| 6 | 2013 | 9 | |
| 7 | 2013 | 1 | |
| 8 | OceanVAR software for use with NEMO: documentation and test guide. | 2012 | 1 |
| 9 | 2012 | 16 | |
| 10 | HPC computation issues of the incremental 3D variational data assimilation scheme in OceanVar software | 2012 | 18 |
| 11 | 2011 | 13 | |
| 12 | 2011 | 3 | |
| 13 | 2008 | 8 | |
| 14 | 2008 | 1 | |
| 15 | 2007 | 5 | |
| 16 | 2006 | 4 | |
| 17 | 2006 | 11 | |
| 18 | 2006 | 22 | |
| 19 | 2003 | 0 | |
| 20 | 1999 | 51 |
About Luisa D’Amore
Luisa D’Amore is a scholar working on Mathematical Physics, Hardware and Architecture, Numerical Analysis, Atmospheric Science and Oceanography, having authored 56 papers that have together received 472 indexed citations. Recurring topics across this work include Meteorological Phenomena and Simulations (16 papers), Numerical methods in inverse problems (11 papers), Distributed and Parallel Computing Systems (9 papers), Reservoir Engineering and Simulation Methods (8 papers), Parallel Computing and Optimization Techniques (7 papers), Statistical and numerical algorithms (5 papers), Iterative Methods for Nonlinear Equations (5 papers) and Image and Signal Denoising Methods (5 papers). The work is most often cited by research in Numerical Analysis (64 citations), Modeling and Simulation (39 citations), Hardware and Architecture (50 citations), Atmospheric Science (117 citations) and Computational Theory and Mathematics (67 citations). Luisa D’Amore has collaborated with scholars based in Italy, United States and United Kingdom. Frequent co-authors include A. Murli, Giuliano Laccetti, Rossella Arcucci, Livia Marcellino, Salvatore Cuomo, Ardelio Galletti, Diego Romano, Giuseppe Scotti, Ralf Toumi and Valentina De Simone. Their work appears in journals such as Journal of Computational and Applied Mathematics, ACM Transactions on Mathematical Software, Inverse Problems, Journal of Scientific Computing and Parallel Computing.
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.