M.Tech in Geomechanics for Mineral & Energy Resources @ IIT Kharagpur

GitHub • LinkedIn • Email

I am a computational engineer interested in solving engineering problems through mathematics, numerical methods, scientific programming, and data.

My background spans geomechanics, reservoir engineering, numerical simulation, computational fluid dynamics, geostatistics, and data engineering. I enjoy turning physical problems into reproducible computational workflows and using simulation and analysis to understand engineering behavior.

Currently, I am exploring Scientific Machine Learning (SciML) and computational approaches for engineering and materials-related problems.

• Numerical simulation and mathematical modelling
• Partial differential equations and numerical methods
• Finite Volume Methods and Computational Fluid Dynamics
• Reservoir simulation and subsurface modelling
• Geomechanics and engineering data analysis
• Scientific Python and reproducible computational workflows
• Statistical and data-driven modelling
• Scientific Machine Learning / Physics-informed methods

Programming & Scientific Computing
Python • MATLAB • NumPy • SciPy • pandas • Matplotlib • Jupyter

Numerical & Engineering Modelling
Finite Volume Methods • CFD • Reservoir Simulation • MRST • Geostatistics • Geomechanics

Data & Automation
SQL • Apache Airflow • ETL • Data Validation • Workflow Automation

Development
Git • Linux • VS Code

Numerical solutions of fundamental fluid-flow PDEs using Python and Jupyter, covering convection, diffusion, Burgers' equation, Laplace, Poisson, and 2D Navier–Stokes problems.

Focus: numerical methods • PDEs • stability • convergence • scientific Python

Python-based reservoir engineering utilities covering PVT analysis, volumetrics, well testing, material balance, decline-curve analysis, and fluid-flow calculations.

Focus: engineering computation • Python • modelling • data analysis

Python implementations of geophysical methods and workflows involving FFT-based analysis, seismic processing, inversion, geomodelling, and machine learning.

Focus: scientific computing • signal processing • inversion • geomodelling

Python workflows for spatial/subsurface data analytics and geostatistics, including variogram analysis, variogram modelling, and kriging.

Focus: spatial statistics • geostatistics • Python • subsurface data

Physical Problem
      ↓
Mathematical Formulation
      ↓
Numerical Method
      ↓
Python Implementation
      ↓
Simulation
      ↓
Sensitivity / Data Analysis
      ↓
Validation
      ↓
Engineering Insight

I am particularly interested in understanding why a computational model works, when it fails, and how numerical results can be validated against physical expectations.

• Computational Materials Science
• Computational Mechanics
• Numerical Linear Algebra
• Scientific Machine Learning
• Physics-Informed Machine Learning
• Numerical PDEs
• Multiphysics Modelling
• Engineering AI/ML

Building deeper intuition at the intersection of physics, mathematics, computation, and data.