Differential equations
Ordinary (ODEs), partial (PDEs), stochastic (SDEs) and fractional (FDEs) differential equations; reaction–diffusion systems; $p$-Laplacian models.
Applied Mathematics · Ph.D.
Doctor of Philosophy in Applied Mathematics
Mathematical Modeling, Numerical Analysis and Scientific Machine Learning for Linear/Nonlinear Dynamical Systems.
MAIS Laboratory · AMNEA Team
Faculty of Sciences and Techniques of Errachidia (FSTE),
Moulay Ismail University of Meknes (UMI), Morocco
Dr. Achraf ZINIHI is an applied mathematician whose research lies at the intersection of mathematical modeling, numerical analysis, scientific computing, and scientific machine learning for nonlinear dynamical systems.
He received his Ph.D. in Applied Mathematics from the Faculty of Sciences and Techniques of Errachidia (FSTE), Moulay Ismail University of Meknes (UMI), Morocco, where he was a member of the Mathematics, Computer Science and Applications Doctoral Program (Mathématiques, Informatique et Applications) and the MAIS Laboratory (Mathématiques Appliquées et Informatiques des Systèmes), within the AMNEA research team (Analyse Mathématique et Numérique des EDPs et Applications).
He obtained his Master of Science in Applied Mathematics from FSTE-UMI in 2022. His master's research focused on optimal control and reaction-diffusion epidemic models involving fractional differential equations, laying the foundation for his doctoral research in mathematical modeling and computational methods for complex dynamical systems.
His research combines rigorous mathematical analysis with the development of advanced computational methods for ordinary, partial, stochastic, and fractional differential equations. His work spans structure-preserving numerical methods, optimal control, inverse problems, reaction-diffusion systems, and modern data-driven techniques including Physics-Informed Neural Networks (PINNs), Neural Ordinary Differential Equations (Neural ODEs), and Koopman operator-based learning. He is particularly interested in designing computational algorithms that preserve the underlying mathematical properties of dynamical systems while enabling efficient simulation, parameter estimation, forecasting, and predictive modeling.
Dr. ZINIHI has authored numerous publications in international peer-reviewed journals and has participated in several international research collaborations and mobility programs in France, Germany, Portugal, and Turkey. He has also served as a reviewer for leading journals in applied mathematics, numerical analysis, and scientific computing, and has taught undergraduate and graduate courses in applied mathematics, numerical analysis, image processing, and fractional differential equations.
In recognition of his academic achievements, he has received several competitive awards and fellowships, including the National Centre for Scientific and Technical Research (CNRST) PhD Associate Scholarship (PASS), French Government Scholarships for research mobility, and the European Mathematical Society Committee for Developing Countries (EMS CDC) Grant.
Nonstandard finite difference (NSFD) schemes, PINNs, Neural ODEs and Koopman operator-based learning for simulation, parameter estimation and prediction, with applications to epidemiological, biological and physical systems.
Ordinary (ODEs), partial (PDEs), stochastic (SDEs) and fractional (FDEs) differential equations; reaction–diffusion systems; $p$-Laplacian models.
Structure-preserving NSFD schemes, simulation, parameter estimation, forecasting.
Optimal control theory, necessary optimality conditions, epidemic control strategies.
Physics-Informed Neural Networks (PINNs), Neural ODEs, Koopman operator-based learning.
Phase portrait: trajectories spiralling into an equilibrium.
SIR epidemic: susceptible (S), infected (I) and recovered (R).
Heat diffusion smoothing an initial profile.
Reaction–diffusion front with $p$-Laplacian diffusion.
Sample paths scattering around the deterministic mean.
Caputo relaxation with memory, for α = 1, 0.8, 0.6, 0.4.
Pattern formation in a coupled PDE system.
Infection peak with and without a control strategy.
Schematic: a network converging to the solution of an ODE.
Nonlinear dynamics become linear in Koopman coordinates: straight lines on a log scale.
Illustration: points transported by a continuous learned flow.
SciML: the model fit converges to noisy epidemic data.
Papers, chapters, preprints and talks per year, colored by type.
Co-authors around the author; node size grows with joint papers.
Core methods and application areas of the research.
The full list is available on my profiles: Google Scholar · Scopus · Web of Science · arXiv · ResearchGate · MathSciNet · ORCID
FSTE, UMI, Morocco. MAIS Laboratory, AMNEA group.
Thesis: “Optimal Control and Neural Networks Applied to Epidemics: Theoretical Analysis and Numerical Simulations.”
FSTE, UMI.
FSTE, UMI.
FSTE, UMI.
High School Mohamed V, Tan-Tan, Morocco.
Doctoral mobility, Laboratory of Applied Mathematics (LMAH, FR-CNRS-3335).
Two-month research stay, School of Mathematics and Natural Sciences.
International Workshop on Optimization, Dynamics and Convex Analysis (WODCA'2025). Talk on necessary optimality conditions for a Caputo-fractional reaction–diffusion SIR model with the $p$-Laplacian operator.
Doctoral mobility, LMAH.
International Communication in the New World Congress (ICNWC'24). Talk on multi-city epidemic modeling with population migration using neural network–based approaches.
Excellence scholarship of the National Centre for Scientific and Technical Research (CNRST), Morocco.
Research stay at Le Havre Normandie, funded by the Embassy of France and the French Institute of Morocco with CNRST.
Covering accommodation, travel and local transport.
To attend the International Workshop on Optimization, Dynamics and Convex Analysis (WODCA'2025), Aveiro, Portugal.
Best PhD student presentation at the International Meeting on Applied Mathematics in Errachidia (IMAME'2025).
Research stay at Le Havre Normandie.
National Office of Academic, Social and Cultural Works (ONOUSC), Morocco.
Students Reward Programme, MSc major, FSTE.
a.zinihi@edu.umi.ac.ma achrafzinihi99@gmail.com
Google Scholar ORCID Scopus Web of Science ResearchGate MathSciNet LinkedIn