ACADEMICS

M. Tech (Computational Engineering)

In addition to a background in mathematics and computer science, a Computational Engineering graduate must have a thorough education in an application area (engineering discipline or science). His mathematical knowledge will be sufficient to model technological and scientific problems. Knowledge of computer science, and in particular numerical algorithms, software design and visualization, enable the Computational Engineering graduate to make efficient use of computers. A graduate knows how to find and exploit software (-packages) for a certain task. A Computational Engineering student performs interdisciplinary work in mathematics, computer science and an application area. He is trained to communicate with and team with an engineer or physicist and/or a computer scientist or mathematician to solve difficult practical problems.

Relevant background

We describe below the background that a well-prepared undergraduate will have had before entering a Computational Engineering program. It is expected that many students will be strong in several of the areas but may need to acquire expertise in other areas during the initial stages of graduate study.

1. Mathematics and computer science

Calculus
Basic applied math
Linear algebra
Real/complex analysis
Software design, programming, and testing
Data structures and algorithms
Numerical analysis

2. Application area: basic knowledge in an application area such as

Physics
Chemistry
Computer science (for example, data mining)
Fluid dynamics
Thermodynamics

Core areas for graduate curriculum

1. Mathematics and computer science Numerical analysis (linear algebra and optimization, ordinary and partial differential equations).

Applied mathematics (ordinary differential equations, dynamical systems, partial differential equations, mathematical modeling).

Computing (languauges/operating systems/networking; parallel/distributed)

Data Analysis (visualization, statistical methods) .

It is observed that in many institutions there is much redundancy and overlap between courses for example in numerical analysis or applied mathematics being taught in various engineering departments and mathematics. It may be advantageous to have a single core track, perhaps adding special sections for discipline-specific material if that is deemed to be necessary. This has the obvious advantage of cost-effectiveness, enabling even relatively small institutions to start a Computational Engineering program. Perhaps even more importantly, it gives the students a common educational foundation for the more advanced courses, and exposes them to other students and faculty with a wide variety of interests in computer science, mathematics, science and engineering.

2. Application areas. It is absolutely essential that interdisciplinary collaboration be an integral part of the curriculum and the thesis research. Courses should include projects and presentations whenever possible. A COMPUTATIONAL ENGINEERING graduate should have working knowledge in an application area like:

Computational physics
Physics of the atmosphere / weather forecast
Astronomy
Computational chemistry
Computational fluid dynamics
Control
Structural dynamics
Bioengineering
Acoustics
Reactive flows
Electromagnetics
Quantum mechanics
Reservoir engineering
Molecular biology
Electronic design automation
Circuit simulation
Semiconductor simulation

Interdisciplinary collaboration can be accomplished via participation in a multidisciplinary research team and/or internship at a National Laboratory or in industry.

Admission Requirements and General Guidelines

Candidates with B.E/.B.Tech degree in any engineering discipline with good aptitude in mathematics or candidates with M.Sc. Mathematics with good programming knowledge in C are eligible to apply.

Selection will be based on GATE score and performance in Aptitude test and Interview.

The interview board will comprise the members of the centre, two internal subject experts from the university, Dean of Academic Affairs, an external expert and an expert from mathematics department.

Stipend will be given for candidate with GATE score.

Sponsored candidates from Academic Institutions and R & D establishments are eligible to apply but no stipend will be given.

For external candidates without salary, partial assistance may be given through teaching assistantship or project associateship if available.

Fee Structure: The fees for the regular candidates will differ from those of the sponsored candidates. The final decision has to be taken by the University.

One copy of the thesis will be sent out to an expert from major institutions like IITs / IISc / NIT etc. for assessment.

It is essential that the student should have submitted a paper to a Journal by the end of the course.

A Departmental Post Graduate Committee (DPGC) committee will monitor the progress of the thesis work of the students. The committee must periodically review the performance and progress of the students and submit recommendations to the Dean (Academic Affairs) and the faculty associated with the program.

Students with good performance may be absorbed as faculty in the Centre and other University departments and they will be allowed / asked to register and work towards their Ph.D degree in the Centre.