Safa Jamali

1.7k total citations
53 papers, 1.4k citations indexed

About

Safa Jamali is a scholar working on Fluid Flow and Transfer Processes, Materials Chemistry and Statistical and Nonlinear Physics. According to data from OpenAlex, Safa Jamali has authored 53 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Fluid Flow and Transfer Processes, 25 papers in Materials Chemistry and 13 papers in Statistical and Nonlinear Physics. Recurrent topics in Safa Jamali's work include Rheology and Fluid Dynamics Studies (31 papers), Material Dynamics and Properties (20 papers) and Model Reduction and Neural Networks (13 papers). Safa Jamali is often cited by papers focused on Rheology and Fluid Dynamics Studies (31 papers), Material Dynamics and Properties (20 papers) and Model Reduction and Neural Networks (13 papers). Safa Jamali collaborates with scholars based in United States, South Korea and Iran. Safa Jamali's co-authors include João M. Maia, Arman Boromand, Mohammadamin Mahmoudabadbozchelou, John F. Brady, George Em Karniadakis, Robert C. Armstrong, Gareth H. McKinley, Lilian C. Hsiao, María C. Paiva and J. A. Covas and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Nature Communications.

In The Last Decade

Safa Jamali

51 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Safa Jamali United States 21 612 498 363 192 189 53 1.4k
Marco Ellero United Kingdom 24 453 0.7× 463 0.9× 1.1k 3.2× 51 0.3× 310 1.6× 84 1.7k
Volfango Bertola United Kingdom 27 335 0.5× 351 0.7× 1.2k 3.3× 61 0.3× 447 2.4× 100 2.2k
Ryohei Seto Japan 15 944 1.5× 659 1.3× 985 2.7× 33 0.2× 196 1.0× 35 1.8k
Douglas A. Reinelt United States 19 665 1.1× 201 0.4× 477 1.3× 33 0.2× 381 2.0× 34 1.4k
Pierre Saramito France 16 218 0.4× 786 1.6× 703 1.9× 33 0.2× 253 1.3× 25 1.3k
Élisabeth Lemaire France 25 523 0.9× 474 1.0× 850 2.3× 38 0.2× 636 3.4× 63 1.8k
B. Mena Mexico 20 105 0.2× 529 1.1× 494 1.4× 88 0.5× 354 1.9× 47 1.1k
V. M. Entov Russia 18 190 0.3× 615 1.2× 803 2.2× 56 0.3× 333 1.8× 97 2.0k
B Brûlé Netherlands 21 251 0.4× 1.1k 2.2× 712 2.0× 25 0.1× 289 1.5× 54 1.7k
Márcio S. Carvalho Brazil 31 576 0.9× 677 1.4× 1.3k 3.7× 31 0.2× 569 3.0× 172 3.2k

Countries citing papers authored by Safa Jamali

Since Specialization
Citations

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

Fields of papers citing papers by Safa Jamali

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Safa Jamali

This figure shows the co-authorship network connecting the top 25 collaborators of Safa Jamali. A scholar is included among the top collaborators of Safa Jamali based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Safa Jamali. Safa Jamali is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jamali, Safa, et al.. (2025). UniFIDES: Universal fractional integro-differential equations solver. SHILAP Revista de lepidopterología. 3(1).
2.
Wagner, Norman J., et al.. (2024). Data-driven constitutive meta-modeling of nonlinear rheology via multifidelity neural networks. Journal of Rheology. 68(5). 679–693. 3 indexed citations
3.
Jamali, Safa, et al.. (2024). Data-driven techniques in rheology: Developments, challenges and perspective. Current Opinion in Colloid & Interface Science. 75. 101873–101873. 8 indexed citations
4.
Armstrong, Matthew, et al.. (2023). A fully physiologically-informed time- and rate-dependent hemorheological constitutive model. Journal of Rheology. 67(3). 775–775. 2 indexed citations
5.
Burpo, F. John, et al.. (2023). Characterizing blood hysteresis via tensorial thixo-elasto-viscoplastic modeling. Physics of Fluids. 35(11). 5 indexed citations
6.
Jamali, Safa, et al.. (2023). A rheologist's guideline to data-driven recovery of complex fluids' parameters from constitutive models. Digital Discovery. 2(4). 915–928. 7 indexed citations
7.
Jamali, Safa, et al.. (2023). Fractional rheology-informed neural networks for data-driven identification of viscoelastic constitutive models. Rheologica Acta. 62(10). 557–568. 17 indexed citations
8.
Li, He, et al.. (2022). Circulating cell clusters aggravate the hemorheological abnormalities in COVID-19. Biophysical Journal. 121(18). 3309–3319. 8 indexed citations
9.
Jamali, Safa, et al.. (2022). Topological origins of yielding in short-ranged weakly attractive colloidal gels. The Journal of Chemical Physics. 158(1). 14903–14903. 5 indexed citations
10.
Jamali, Safa, et al.. (2022). Solvation Thermodynamics of Solutes in Water and Ionic Liquids Using the Multiscale Solvation-Layer Interface Condition Continuum Model. Journal of Chemical Theory and Computation. 18(9). 5539–5558. 3 indexed citations
11.
Mahmoudabadbozchelou, Mohammadamin, et al.. (2022). Data-driven selection of constitutive models via rheology-informed neural networks (RhINNs). Rheologica Acta. 61(10). 721–732. 20 indexed citations
12.
Jamali, Safa, et al.. (2021). Life and death of colloidal bonds control the rate-dependent rheology of gels. Nature Communications. 12(1). 4274–4274. 39 indexed citations
13.
Mahmoudabadbozchelou, Mohammadamin & Safa Jamali. (2021). Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids. Scientific Reports. 11(1). 12015–12015. 44 indexed citations
14.
Mahmoudabadbozchelou, Mohammadamin, George Em Karniadakis, & Safa Jamali. (2021). nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling. Soft Matter. 18(1). 172–185. 76 indexed citations
15.
Jamali, Safa, et al.. (2020). A hydrodynamic model for discontinuous shear-thickening in dense suspensions. Journal of Rheology. 64(2). 379–394. 31 indexed citations
16.
Jamali, Safa, Robert C. Armstrong, & Gareth H. McKinley. (2019). Multiscale Nature of Thixotropy and Rheological Hysteresis in Attractive Colloidal Suspensions under Shear. Physical Review Letters. 123(24). 248003–248003. 38 indexed citations
17.
Hsiao, Lilian C., Safa Jamali, Emmanouil Glynos, et al.. (2017). Rheological State Diagrams for Rough Colloids in Shear Flow. Physical Review Letters. 119(15). 158001–158001. 104 indexed citations
18.
Jamali, Safa. (2015). Rheology of Colloidal Suspensions: A Computational Study. OhioLink ETD Center (Ohio Library and Information Network). 2 indexed citations
19.
Jamali, Safa, et al.. (2015). Generalized mapping of multi-body dissipative particle dynamics onto fluid compressibility and the Flory-Huggins theory. The Journal of Chemical Physics. 142(16). 164902–164902. 38 indexed citations
20.
Jamali, Safa, et al.. (2014). Dissipative Particle Dynamics simulation of colloidal suspensions. Bulletin of the American Physical Society. 2014. 1 indexed citations

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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