Malú Grave
Impact in
- Modeling and Simulation top 10%
- COVID-19 epidemiological studies
- Mathematical Biology Tumor Growth
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- Model Reduction and Neural Networks
Papers in
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- COVID-19 epidemiological studies 4
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- Model Reduction and Neural Networks 4
- Co-authors
- Álvaro L. G. A. Coutinho (10 shared papers)Alex Viguerie (3 shared papers)Alessandro Reali (3 shared papers)Roberto F. S. Andrade (2 shared papers)Guillermo Lorenzo (1 shared paper)José Manuel Mendes (1 shared paper)Maria Yuri Ichihara (1 shared paper)Michel Tosin (1 shared paper)
- Journals
- Computers & Fluids (3 papers)Computer Methods in Applied Mechanics and Engineering (2 papers)Journal of Biomechanical Engineering (1 paper)GEM - International Journal on Geomathematics (1 paper)Archive of Applied Mechanics (1 paper)
- Partner nations
- BrazilItalyUnited States
In The Last Decade
Malú Grave
13 papers receiving 93 citations
Peers
Comparison fields: 5 of 47
- Modeling and Simulation 25
- Statistical and Nonlinear Physics 25
- Computational Mechanics 33
- Ocean Engineering 16
- Statistics, Probability and Uncertainty 6
Countries citing papers authored by Malú Grave
This map shows the geographic impact of Malú Grave'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 Malú Grave with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Malú Grave more than expected).
Fields of papers citing papers by Malú Grave
This network shows the impact of papers produced by Malú Grave. 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 Malú Grave. The network helps show where Malú Grave may publish in the future.
Co-authors
The 21 scholars most cited alongside Malú Grave, 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 | 2022 | 23 | |
| 2 | 2020 | 22 | |
| 3 | 2022 | 10 | |
| 4 | 2022 | 9 | |
| 5 | 2022 | 9 | |
| 6 | 2022 | 5 | |
| 7 | 2024 | 3 | |
| 8 | 2021 | 3 | |
| 9 | 2023 | 3 | |
| 10 | 2025 | 2 | |
| 11 | 2024 | 2 | |
| 12 | 2019 | 2 | |
| 13 | 2022 | 1 | |
| 14 | 2024 | 0 |
About Malú Grave
Malú Grave is a scholar working on Modeling and Simulation, Statistical and Nonlinear Physics, Computational Mechanics, Epidemiology and Molecular Biology, having authored 14 papers that have together received 94 indexed citations. Recurring topics across this work include COVID-19 epidemiological studies (4 papers), Model Reduction and Neural Networks (4 papers), Lattice Boltzmann Simulation Studies (3 papers), Data-Driven Disease Surveillance (3 papers), Heat shock proteins research (2 papers), Fluid Dynamics and Heat Transfer (2 papers), Hydraulic Fracturing and Reservoir Analysis (2 papers) and Hydrology and Sediment Transport Processes (1 paper). The work is most often cited by research in Modeling and Simulation (25 citations), Statistical and Nonlinear Physics (25 citations), Computational Mechanics (33 citations), Ocean Engineering (16 citations) and Statistics, Probability and Uncertainty (6 citations). Malú Grave has collaborated with scholars based in Brazil, Italy and United States. Frequent co-authors include Álvaro L. G. A. Coutinho, Alex Viguerie, Alessandro Reali, Roberto F. S. Andrade, Guillermo Lorenzo, José Manuel Mendes, Maria Yuri Ichihara, Michel Tosin, Américo Cunha and Marcos Barreto. Their work appears in journals such as Computers & Fluids, Computer Methods in Applied Mechanics and Engineering, Journal of Biomechanical Engineering, GEM - International Journal on Geomathematics and Archive of Applied Mechanics.
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.