Author

Hani Cheikh Sleiman

University College LondonORCID 0009-0000-1380-4284Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioengineering· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:statistical model· 1

Frequent Co-Authors

Brian Ritchie DavidsoniD· 1 shared Diana C. de OliveiraiD· 1 shared Joseph JacobiD· 1 shared Kevin Mattheus MoermaniD· 1 shared Nesrin MoğulkoçiD· 1 shared Rebecca J. ShipleyiD· 1 shared Simon Walker‐SamueliD· 1 shared

Activity by Year

2025-2025 · peak 1/yr

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7 top collaborators · 21 mutual co-author links

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1 shared paperBioengineering

Brian Ritchie DavidsonORCID

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1 paper

Recent·Most cited

BioengineeringRoyal Society Open SciencePeer-reviewedAug 1, 2025

Tube2FEM: a general-purpose highly-automated pipeline for flow related processes in (embedded) tubular objects

Hani Cheikh Sleiman, Kevin Mattheus Moerman, Diana C. de Oliveira…·Rebecca J. Shipley

This paper introduces a highly-automated pipeline for generating simulation-ready meshes of tubular structures, enabling CFD simulations across various biomedical and industrial applications.

3 citations

deep learningstatistical model

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