Advanced Computational Techniques Course | IIT Kharagpur | Numerical Methods
Course Details
| Exam Registration | 71 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 4 weeks |
| Categories | Mathematics |
| Credit Points | 1 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 13 Feb 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 16 Feb 2026 |
| Exam Date | 29 Mar 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Master Advanced Computational Techniques: A 4-Week Deep Dive with an IIT Kharagpur Expert
In the realms of engineering, physics, and applied mathematics, the ability to translate complex physical phenomena into solvable numerical models is a superpower. While basic numerical methods courses are plentiful, a structured pathway to advanced computational modeling and scientific computing is rare. This is where the specialized course "Advanced Computational Techniques" comes in, meticulously designed and delivered by a seasoned expert from India's premier institute.
Your Guide: Prof. Somnath Bhattacharyya, IIT Kharagpur
Learning from an experienced practitioner is invaluable. This course is led by Prof. Somnath Bhattacharyya, a senior professor in the Department of Mathematics at IIT Kharagpur. With a specialization in Applied Mathematics, Prof. Bhattacharyya brings over 28 years of teaching experience to the table, regularly instructing B.Tech students on Integral Transforms, Partial Differential Equations (PDEs), and Numerical Solutions of PDEs.
His expertise is not confined to the classroom. As an active researcher with more than 160 publications in reputed international journals, his work focuses on the numerical solutions of PDEs. He has guided 19 PhD students to completion and undertaken several sponsored research projects. His teaching prowess extends to AICTE courses, NPTEL sessions, and GIAN programs, and he has received fellowships for research collaborations in the USA, UK, and Germany. You will be learning from a true authority in the field.
Course Overview: Bridging Theory and Implementation
This course is designed as a foundational yet advanced introduction for engineering and science students. It moves beyond theoretical concepts to emphasize the practical implementation of numerical methods. In today's world, scientific computing is integral to disciplines ranging from aerospace to chemical engineering. Completing this course will equip you with the skills to handle advanced computational tools confidently.
Intended Audience: Undergraduate or Postgraduate students of any Engineering stream, Mathematics, Physics, and specifically PG students of Mathematics, Mechanical, Aerospace, or Chemical Engineering.
Prerequisites: A basic undergraduate course in Mathematics/Calculus and elementary knowledge of numerical methods.
Detailed 4-Week Course Layout
The course is structured to build your knowledge systematically over four intensive weeks.
Week 1: Foundations and Approximation
- Overview of Elementary Numerical Methods
- Hermite Interpolation and Cubic Splines
- Numerical Quadrature and Gauss Quadrature
Week 2: Linear Algebra & Systems
- Least Square Approximation
- Solving Linear Systems of Equations: LU-decomposition, Tri-diagonal systems
- Iterative Methods: SOR, Conjugate Gradient Method
- Eigenvalue Computation via the Power Method
Week 3: Ordinary Differential Equations (ODEs)
- Initial Value Problems (IVPs): Predictor-Corrector methods, Runge-Kutta method
- Analysis of Stability and Truncation Error
- Linear Boundary Value Problems (BVPs) solved by the Finite Difference Method
Week 4: Partial Differential Equations (PDEs)
- Non-linear Boundary Value Problems and Iterative methods
- Advection-Diffusion Equations: Implicit Scheme, Crank-Nicolson Scheme
- von-Neumann Stability Analysis
- Linear Hyperbolic PDEs: Upwind scheme, Lax scheme
- Introduction to Non-linear Burgers Equations and Iterative schemes
Key Learning Outcomes & Resources
By the end of this course, you will have a strong grasp of implementing critical numerical techniques for interpolation, solving linear systems, ODEs, and PDEs—the backbone of computational simulation. All methods are illustrated with worked examples to cement understanding.
Recommended Textbooks:
| Book Title | Authors |
|---|---|
| Numerical Methods for Engineers and Scientists | J.D. Hoffman |
| Numerical Methods for Elliptic and Parabolic Partial Differential Equations | P. Knabner & L. Angermann |
Why Enroll in This Course?
This course fills a crucial gap for students and professionals looking to advance beyond basic numerical analysis. It provides the practical, implementation-focused knowledge required for research, higher studies, and roles in computational fields. Under the guidance of Prof. Bhattacharyya, you gain not just formulas, but the insight and approach of an expert who has contributed significantly to the field. Whether you aim to work in computational fluid dynamics, finite element analysis, or any simulation-driven domain, this course offers a powerful launchpad.
Duration: 4 Weeks | Level: Undergraduate/Postgraduate
Take the next step in mastering the computational techniques that drive modern scientific and engineering breakthroughs.
Enroll Now →