Bassem Akoush

417 citations
10 papers · 288 · 1 hit paper · h-index 8

Impact in

Papers in

Bassem Akoush

8 papers receiving 281 citations

Hit Papers

A Review of Physics-Informed Machine Learning in Fluid Mechanics 2023 · 115 citations
1150+1+2Years since publication255075100

Peers

Bassem Akoush
Comparison fields: 5 of 59
  • Fluid Flow and Transfer Processes 42
  • Renewable Energy, Sustainability and the Environment 79
  • Computational Mechanics 89
  • Statistical and Nonlinear Physics 49
  • Artificial Intelligence 67
Replace Zhihu Li with:
Zhihu Li China
S.A. Klein Netherlands
A. Bello-García Spain
Emiliano Casati Netherlands
Magnus Genrup Sweden
Sean T. Smith United States
Arturo Pacheco-Vega United States
Nicolas Perrin France
R. Cònsul Spain
Bassem Akoush relative to Zhihu Li China Zhihu Li's profile →
Citations per field
00.5×4.9×
Zhihu Li · 1×
Citations per year

Countries citing papers authored by Bassem Akoush

Since Specialization
Citations

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

Fields of papers citing papers by Bassem Akoush

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Bassem Akoush, 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 Bassem Akoush Line = papers co-authored together Bassem Akoush links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1
A Review of Physics-Informed Machine Learning in Fluid Mechanics
Hit paper breakdown →
2023115
2 202059
3 202239
4 202124
5 202219
6 202113
7 202011
8 20218
9 20250
10 20230

About Bassem Akoush

Bassem Akoush is a scholar working on Computational Mechanics, Fluid Flow and Transfer Processes, Renewable Energy, Sustainability and the Environment, Safety, Risk, Reliability and Quality and Artificial Intelligence, having authored 10 papers that have together received 288 indexed citations. Recurring topics across this work include Combustion and flame dynamics (4 papers), Advanced Combustion Engine Technologies (4 papers), Photovoltaic System Optimization Techniques (4 papers), Solar Thermal and Photovoltaic Systems (3 papers), Fire dynamics and safety research (2 papers), Solar Radiation and Photovoltaics (2 papers), Radiative Heat Transfer Studies (1 paper) and Nuclear Engineering Thermal-Hydraulics (1 paper). The work is most often cited by research in Fluid Flow and Transfer Processes (42 citations), Renewable Energy, Sustainability and the Environment (79 citations), Computational Mechanics (89 citations), Statistical and Nonlinear Physics (49 citations) and Artificial Intelligence (67 citations). Bassem Akoush has collaborated with scholars based in Egypt, United States and Australia. Frequent co-authors include Matthias Ihme, Wai Tong Chung, Muhammed A. Hassan, Mohamed Abubakr, A. Khalil, Guillaume Vignat, Edna R. Toro, Pietro Elia Campana, Assaad R. Masri and Mohy S. Mansour. Their work appears in journals such as Renewable Energy, Proceedings of the Combustion Institute, Combustion and Flame, Energies and Fuel.

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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