T. Tachim Medjo

854 total citations
104 papers, 565 citations indexed

About

T. Tachim Medjo is a scholar working on Applied Mathematics, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, T. Tachim Medjo has authored 104 papers receiving a total of 565 indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Applied Mathematics, 50 papers in Computational Theory and Mathematics and 42 papers in Materials Chemistry. Recurrent topics in T. Tachim Medjo's work include Advanced Mathematical Modeling in Engineering (50 papers), Solidification and crystal growth phenomena (42 papers) and Stability and Controllability of Differential Equations (41 papers). T. Tachim Medjo is often cited by papers focused on Advanced Mathematical Modeling in Engineering (50 papers), Solidification and crystal growth phenomena (42 papers) and Stability and Controllability of Differential Equations (41 papers). T. Tachim Medjo collaborates with scholars based in United States, Cameroon and France. T. Tachim Medjo's co-authors include Gabriel Deugoué, Roger Témam, Ciprian G. Gal, Jie Shen, Eric Simonnet, Louis Tebou, Mohammed Ziane, Caidi Zhao and Andrea Giorgini and has published in prestigious journals such as Journal of Computational Physics, SIAM Journal on Numerical Analysis and Journal of Mathematical Analysis and Applications.

In The Last Decade

T. Tachim Medjo

92 papers receiving 506 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
T. Tachim Medjo United States 13 272 243 203 196 140 104 565
Mădălina Petcu France 11 186 0.7× 122 0.5× 107 0.5× 86 0.4× 42 0.3× 38 375
Michele Coti Zelati United States 15 124 0.5× 252 1.0× 17 0.1× 203 1.0× 31 0.2× 39 498
Nathan Glatt-Holtz United States 13 174 0.6× 214 0.9× 8 0.0× 173 0.9× 351 2.5× 28 547
Evelyn Lunasin United States 11 124 0.5× 358 1.5× 9 0.0× 289 1.5× 34 0.2× 18 687
D. Wirosoetisno United Kingdom 10 88 0.3× 74 0.3× 24 0.1× 64 0.3× 13 0.1× 27 307
Julien Vovelle France 14 149 0.5× 450 1.9× 6 0.0× 121 0.6× 187 1.3× 32 696
Alexey Cheskidov United States 16 117 0.4× 653 2.7× 11 0.1× 319 1.6× 85 0.6× 41 911
Vlad Vicol United States 21 188 0.7× 1.0k 4.2× 11 0.1× 305 1.6× 140 1.0× 49 1.2k
Anna L. Mazzucato United States 14 183 0.7× 385 1.6× 6 0.0× 64 0.3× 55 0.4× 40 653
Francisco Gancedo Spain 18 230 0.8× 790 3.3× 13 0.1× 77 0.4× 12 0.1× 43 937

Countries citing papers authored by T. Tachim Medjo

Since Specialization
Citations

This map shows the geographic impact of T. Tachim Medjo'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 T. Tachim Medjo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Tachim Medjo more than expected).

Fields of papers citing papers by T. Tachim Medjo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by T. Tachim Medjo. 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 T. Tachim Medjo. The network helps show where T. Tachim Medjo may publish in the future.

Co-authorship network of co-authors of T. Tachim Medjo

This figure shows the co-authorship network connecting the top 25 collaborators of T. Tachim Medjo. A scholar is included among the top collaborators of T. Tachim Medjo 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 T. Tachim Medjo. T. Tachim Medjo 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.
2.
Medjo, T. Tachim. (2024). On the stochastic Cahn-Hilliard-Navier-Stokes systems with surfactant. Discrete and Continuous Dynamical Systems. 44(10). 3023–3076.
3.
Giorgini, Andrea, et al.. (2023). Existence and regularity of strong solutions to a nonhomogeneous Kelvin-Voigt-Cahn-Hilliard system. Journal of Differential Equations. 372. 612–656. 1 indexed citations
4.
Deugoué, Gabriel & T. Tachim Medjo. (2023). Large deviation for a 3D globally modified Cahn–Hilliard–Navier–Stokes model under random influences. Stochastic Processes and their Applications. 160. 33–71.
5.
Medjo, T. Tachim. (2023). A stochastic Allen–Cahn–Navier–Stokes model with inertial effects driven by multiplicative noise of jump type. Mathematische Nachrichten. 296(9). 4386–4428. 1 indexed citations
6.
Deugoué, Gabriel, et al.. (2021). Approximation of a stochastic two-phase flow model by a splitting-up method. Communications on Pure & Applied Analysis. 20(3). 1135–1170. 1 indexed citations
7.
Medjo, T. Tachim. (2021). Large deviation principles for a 2D stochastic Allen–Cahn–Navier–Stokes driven by jump noise. Stochastics and Dynamics. 22(4). 1 indexed citations
8.
Deugoué, Gabriel & T. Tachim Medjo. (2020). Large deviation for a 2D Cahn-Hilliard-Navier-Stokes model under random influences. Journal of Mathematical Analysis and Applications. 486(1). 123863–123863. 3 indexed citations
9.
Deugoué, Gabriel, et al.. (2020). Existence of a solution to the stochastic nonlocal Cahn–Hilliard Navier–Stokes model via a splitting-up method. Nonlinearity. 33(7). 3424–3469. 8 indexed citations
10.
Deugoué, Gabriel, et al.. (2020). Fully discrete finite element approximation of the stochastic Cahn–Hilliard–Navier–Stokes system. IMA Journal of Numerical Analysis. 41(4). 3046–3112. 1 indexed citations
11.
12.
Medjo, T. Tachim. (2019). Weak solution of a stochastic 3D Cahn-Hilliard-Navier-Stokes model driven by jump noise. Journal of Mathematical Analysis and Applications. 484(1). 123680–123680. 2 indexed citations
13.
Deugoué, Gabriel & T. Tachim Medjo. (2018). Convergence of the solution of the stochastic 3D globally modified Cahn–Hilliard–Navier–Stokes equations. Journal of Differential Equations. 265(2). 545–592. 24 indexed citations
14.
Medjo, T. Tachim. (2016). Attractors for a two-phase flow model with delays. Differential and Integral Equations. 29(11/12). 1 indexed citations
15.
Medjo, T. Tachim, et al.. (2015). Approximation of the long-term dynamics of the dynamical system generated by the multilayer quasigeostrophic equations of the ocean. Numerical Methods for Partial Differential Equations. 32(3). 1041–1065. 1 indexed citations
16.
Gal, Ciprian G. & T. Tachim Medjo. (2013). A Navier–Stokes–Voight model with memory. Mathematical Methods in the Applied Sciences. 36(18). 2507–2523. 23 indexed citations
17.
Medjo, T. Tachim. (2010). Second-order Optimality Conditions for Optimal Control of the Primitive Equations of the Ocean with Periodic Inputs. Applied Mathematics & Optimization. 63(1). 75–106. 2 indexed citations
18.
Medjo, T. Tachim. (2007). A Small Eddy Correction Method for a 3D Navier–Stokes-Type System of Equations Related to the Primitive Equations of the Ocean. SIAM Journal on Numerical Analysis. 45(5). 1843–1870. 7 indexed citations
19.
Medjo, T. Tachim. (2002). Numerical solutions of a robust control problem associated with the quasi-geostrophic equation of the ocean. Nonlinear Analysis Real World Applications. 3(3). 317–337. 4 indexed citations
20.
Medjo, T. Tachim. (1995). Vorticity-Velocity formulation for the stationary Navier-Stokes equations: The three-dimensional case. Applied Mathematics Letters. 8(4). 63–66. 7 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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