M.Tech in Computational Engineering and Networking
Computational Engineering is a broad multidisciplinary area that encompasses applications in science/engineering, applied mathematics, numerical analysis and computer science. The M.Tech program is a two year program with a total of 66 credits. The course provides training so that graduates can work on solving difficult practical
problems in close association with engineers, physicists, computer scientists and mathematicians.
Today computer models and computer simulations have become an important part of the research repertoire, supplementing (and in some cases replacing) experimentation. Going from application areas to computational results requires domain expertise, mathematical modeling, numerical analysis, algorithm development, software implementation, program execution, analysis, validation and visualization of results. Computational Engineering involves all of this.
About Computational Engineering
Computational Engineering uses techniques of applied mathematics and computer science for development of problem-solving methodologies and robust tools. These, in turn, become building blocks for solutions to scientific and engineering problems of ever-increasing complexity.
Computational Engineering differs from mathematics and computer science in that analysis and methodologies are directed specifically at the solution of problem classes from science and engineering. Detailed knowledge of and substantial collaboration with these disciplines is generally required.
This interdisciplinary graduate program in Computational Engineering provides students with skills in the following areas.
1. A scientific or engineering discipline
2. Software Design, Development and Verification
3. Applied Mathematics, Numerical Algorithms & Analysis and Computer Implementation
4. High Performance Computing
Why Computational Engineering?
Emergence of high-performance computing has created yet another mode for scientific investigation. Computational simulations now join theoretical analysis and physical experimentation as tools for discovering new knowledge.
Computation simulations enable the study of complex systems and natural phenomena that would be almost impossible to study by direct experimentation. The quest for ever-higher levels of detail and realism in such simulations requires enormous computational capacity. This has provided the impetus for dramatic breakthroughs in computer algorithms and architectures. Due to these advances computational scientists and engineers are now able to solve large-scale problems that were once thought intractable.
All this has created the need for new curriculum to meet the nation's demand for scientists and engineers who have the broad understanding needed to develop and apply these new investigative tools to scientific research and engineering design. Such curricula must involve cross-disciplinary education. Amrita's graduate program in Computational Engineering is unique in its approach to meeting this need.
Foundation Core
| Code | Subject |
L T P |
Credits |
|---|---|---|---|
| CN 611 | Computational Linear Algebra and Applications | 2 0 1 |
3 |
| CN 612 | Engineering Modeling and Partial Differential Equations | 2 0 1 |
3 |
| CN 613 | Computational Optimization Theory - Linear and Non-Linear Methods | 2 0 1 |
3 |
| CN 614 | Advanced Data Structures and Algorithms | 3 0 1 |
4 |
| CN 615 | Probability, Statistics and Applications | 2 0 1 |
3 |
Subject Core
| Code | Subject |
L T P |
Credits |
|---|---|---|---|
| CN 621 | Information Visualization | 2 0 1 |
3 |
| CN 622 | Iterative Methods for Sparse Linear Systems | 3 0 1 |
4 |
| CN 623 | Essentials of Computer Architecture and Software Engineering | 2 0 1 |
3 |
| CN 624 | Seminar on Advanced Topics | 0 0 1 |
1 |
| CN 625 | Computer Networks and High Performance Computing | 3 0 1 |
4 |
| CN 626 | Natural Language Processing | 2 0 1 |
3 |
| CN 627 | Seminar on Advanced Topics | 0 0 1 |
1 |
Electives
| Code | Subject |
L T P |
Credits |
|---|---|---|---|
ELECTIVE-I |
|||
| CN 701 | Data Mining and Applications | 3 0 1 |
4 |
| CN 702 | Computational Chemistry and Molecular Modeling | 3 0 1 |
4 |
ELECTIVE-II |
|||
| CN 703 | Advanced Signal Processing Using Wavelets | 3 0 1 |
4 |
| CN 704 | Understanding Molecular Simulation | 3 0 1 |
4 |
| CN 705 | Level Set Methods and Applications | 3 0 1 |
4 |
ELECTIVE-III |
|||
| CN 706 | Grid Generation Techniques | 3 0 1 |
4 |
| CN 707 | Advanced Image Processing | 3 0 1 |
4 |
| CN 708 | Kernel Methods | 3 0 1 |
4 |
| CN 709 | PDE Constrained Optimization | 3 0 1 |
4 |
ELECTIVE-IV |
|||
| CN 710 | Applied Computational Linguistics | 3 0 1 |
4 |
| CN 711 | Speech Recognition | 3 0 1 |
4 |
| CN 712 | Multiwavelet Theory and Applications | 3 0 1 |
4 |
| CN 713 | Parallel Programming for GPUs | 3 0 1 |
4 |
L = Lecture, T = Tutorial, P = Practicum
Project Work
| Code | Subject |
Credits |
|---|---|---|
| CN 798 | Minor Project | 4 |
| CN 799 | Dissertation | 10 |