Research Profile
I am a graduate researcher in Applied Mathematics and Computational Science at North South University. My academic formation began in engineering, but my principal intellectual interests have since migrated toward linear algebra, partial differential equations, and the mathematical structure underlying physical systems.
My thesis research is situated at the intersection of Computational Fluid Dynamics (CFD) and applied mathematics, with a specific focus on transport phenomena in porous media and nanoparticle-laden flows. Concurrently, I develop machine learning frameworks — including neural differential equations — to augment and accelerate classical numerical methods.
My longer-term research programme aims to bring tools from topological data analysis and differential topology to bear on complex dynamical systems, with the goal of developing geometrically-informed, robust models that transcend the limitations of coordinate-based approaches.
Project Portfolio
Research output spans three core domains. Each entry links to source code, documentation, and reproducible implementations on GitHub.
Computational Fluid Dynamics
Transport phenomena in porous media; nanoparticle suspension flows; finite volume discretization and POD-based model reduction.
Mathematics & Modelling
Linear algebra applications to physical systems; nonlinear PDE analysis; numerical stability and convergence studies.
Machine Learning in Physics
Neural ODEs for dynamical system identification; building energy efficiency prediction; statistical benchmarking of physics-informed architectures.
Skills & Methods
- Linear & Multilinear Algebra
- Partial Differential Equations
- Numerical Analysis
- Finite Volume Methods
- Dynamical Systems
- Python (NumPy, SciPy, Matplotlib)
- PyTorch / TensorFlow
- MATLAB
- LaTeX
- Git / GitHub
- Computational Fluid Dynamics
- Physics-Informed ML
- Porous Media Flows
- Topological Data Analysis
- English — IELTS 8.5 (C2)
- Bengali — Native
- Academic Writing