Hi, I’m Prof. Dr. Mubashir Qayyum
Welcome to MathX — a research lab at the intersection of mathematics and technology. We explore fundamental ideas and build mathematical approaches to real-world challenges. MathX explores into various domains of applied and computational mathematics, with a primary focus on modeling and simulation. The lab's expertise includes fractional and fuzzy modeling of complex phenomena as differential systems. MathX is also actively involved in developing and refining numerical and homotopy-based algorithms. Currently, MathX team is working on fuzzy-fractional modeling in fluid dynamics, higher-dimensional chaotic systems, epidemic modeling, mathematical physics and many more. Additionally, the team is advancing in the field of soft computing by utilizing neural networks, machine and deep learning for modeling and simulation of differential systems. These projects underscore MathX commitment to advancing mathematical research and applying innovative approaches to address real-world challenges across diverse scientific disciplines.
What I Do
Numerical computing
Solving complex engineering, scientific, and mathematical problems using programming tools like MATLAB, Python, C++, or Mathematica
Soft computing
Applying techniques like fuzzy logic, neural networks, genetic algorithms, and probabilistic reasoning to solve complex, imprecise, or real-world optimization problems for clients
Fractional systems
Mathematical Modeling & Simulations: Building and refining ordinary differential equations (ODEs), partial differential equations (PDEs), or fractional calculus models in tools like MATLAB or Julia
Mathematical modeling
Translating real-world problems from industries like finance, engineering, healthcare, and data science into formal equations, simulations, and computational algorithms
Fluid dynamics
Primarily centers on Computational Fluid Dynamics (CFD), mathematical modeling, and numerical analysis
Biological & epidemic modeling
Bridges complex academic concepts and practical, real-world solutions. We use deterministic systems (such as differential equations) and stochastic simulations to analyze how diseases and populations change over time.
My Blog
Fuzzy fractional modeling and analysis of stochastic Fokker Planck equations under uncertainty and memory effects
In this study, the Fokker-Planck model, which describes the time-evolution of probability density functions, is reformulated within a fuzzy-fractional structure to incorporate uncertainties. Triangular fuzzy numbers are employed to represent uncertainty by incorporating fuzzy parameter in the initial conditions. After fuzzification, He-Laplace framework is applied to derive approximate analytical solutions. To evaluate the eciency and reliability of the proposed methodology, residual errors are computed for both upper and lower bound solutions. The numerical results are further analyzed numerically through comparative tables, and visually as 2D, 3D and contour plots. The results demonstrate that the observed errors remain negligible, supporting the ecacy of proposed methodology and validity of obtained solutions. The proposed framework provides an ecient tool for analyzing fuzzy–fractional Fokker–Planck models and can be extended to other nonlinear fuzzy fractional systems.
Contact With Me
Dr Mubashir Qayyum
Professor PhD Approved SupervisorI am available for freelance work. Connect with me via email and call in to my account.
Phone: +012 345 678 90 Email: mubashir@labmathx.org