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Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

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Coverage is partial and says so. A country appears with its research output only once its works have been swept; until then the portal shows whatever the graph happens to hold for it and labels it as exactly that.

Artificial Intelligence-Driven Surrogate Modelling for Nozzle Flow Simulation: A Review of Methods, Applications, and Challenges

2026European Journal of Computer Science and Information TechnologyJournalOpen access

10.37745/ejcsit.2013/vol14n35464 Full text0 citations

Authors

9 authors across 2 institutions in 2 countries.

Usman Abdulrahman SundayFirst author
Aminu Musa Chindo
Hassan Yahaya
- National Space Research and Development Agency (NG)
as printed: Yahaya Hassan
Adam Muhammed Danjuma
Ibrahim Abass
Abdulsalam Anave Sidiqat
Mallum Idris Mambula
Ibrahim Abdullahi
- National Space Research and Development Agency (NG)
Muhammad Abubakar AlhassanSenior author

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

Model Reduction and Neural Networkslevel 3 · 0.72
Computational Fluid Dynamics and Aerodynamicslevel 3 · 0.10
Advanced Multi-Objective Optimization Algorithmslevel 3 · 0.07

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 9, 2026
2026 · European Journal of Computer Science and Information Technology · 0 citations
https://openalex.org/W7162239785

Artificial Intelligence-Driven Surrogate Modelling for Nozzle Flow Simulation: A Review of Methods, Applications, and Challenges · Sankore