Applied & Computational Mathematics

Tobibur Rahman

Researcher & Aspiring Scientist

Hi, I'm an enthusiastic researcher and aspiring scientist exploring how noise and fractional-order effects reshape solitons and other nonlinear wave structures — then carrying those same numerical tools into messier, real-world signals like river floods and shifting land cover.

|φ(x,t)| — periodic soliton amplitude under stochastic perturbation

Research focus

Solitons, noise, and fractional order

Four linked numerical studies on how stochastic noise and fractional-order derivatives change the behaviour of soliton solutions — amplitude, stability, and growth rate.

Noise Effects on Periodic Soliton — numerical simulation plot
Numerical study

Noise Effects on Periodic Soliton

Tracking how stochastic noise intensity κ reshapes the amplitude of a periodic soliton across space and time.

Fractional Order & Noise Affect Soliton — numerical simulation plot
Numerical study

Fractional Order & Noise Affect Soliton

Combining fractional-order derivatives with stochastic noise to see how the two effects compound in soliton evolution.

Modulation Instability with Noise — numerical simulation plot
Numerical study

Modulation Instability with Noise

Growth-rate analysis of modulation instability as a function of noise intensity σ, tracked across real and imaginary components.

Fractional Effect on Solitons — numerical simulation plot
Numerical study

Fractional Effect on Solitons

Comparing soliton solutions Q(x,t) across fractional orders α, from integer order down to near-integer fractional dynamics.

From theory to the field

Applied computational work

The same numerical instincts, pointed at real, messy data.

Dinajpur, Bangladesh

Flood dynamics modelling

Adapted a fractional stochastic PDE model from Maple into MATLAB and Python — debugging the symbolic-to-numeric conversion until it recovered stable solution families for flood behaviour.

MapleMATLABPythonStochastic PDEs
Gazipur District

Land cover change & prediction

Thesis work detecting and predicting land use / land cover change — Random Forest classification over multi-epoch satellite imagery, projected forward with CA-Markov modelling.

Google Earth EngineArcGIS ProRandom ForestCA-Markov

Get in touch

Want to get in touch?
Drop me a line.

If you're interested in joining my research team, collaborating, or just want to talk about fractional PDEs and solitons, I'd love to hear from you.

This form needs a form service (e.g. Formspree) to actually deliver mail on GitHub Pages — see README.md. Prefer email? Use the profiles below.