Master of Technology in Modeling and Simulation
Simulation & scientific computing is the third pillar alongside theory and experiment in today’s science and engineering, and thus, computer-based simulations form an integral part of modern research methodology. In this era of science-driven engineering, the role of scientific research, based on modelling, simulation and design, is of paramount importance. Industries and academics worldwide are gearing up to avail the challenging opportunities provided by this tool. The primary requisite in using the third avenue of research for solving complex problems was the state-of-the-art High-Performance Computing (HPC) centre.
Looking at the multi-disciplinary research done at CMSD and the huge contribution being made by faculty members of various schools, it is decided to start a Four-semester M.Tech. Modeling and Simulation from the premises of CMSD. The objective of the course is to make students ready to take up jobs in the industry and R&D institutions or prepare them for higher studies in their domain of study. The course is designed to give students practical exposure and theoretical rigor equally. Students of this programme will be exposed to emerging areas that require expertise in computational techniques. The HPC resources of CMSD are uniquely suitable for this objective and should prove the ideal platform for this multi-disciplinary programme. The human resources generated from such efforts will be invaluable. The syllabus is designed keeping in mind today’s need with perfect balance of courses from various streams supported by HPC courses as core, and courses in AI and ML as electives.
Mission Statements
MS-1: To provide a platform for multi-disciplinary education and research of global standards using advanced methods and HPC techniques.
MS-2: To carry out discovery-oriented research of international standards.
MS-3: To establish academic and research collaborations with the academia, industries and research institutes of National and International repute.
MS-4: Encourage students in design thinking and practical approaches to learning. Students will be made aware of real-life socio-economic problems to solve using HPC/AI technology learnings.
MS-5: To produce human resources in multi-disciplinary education and High-Performance computing.
Name of the Academic Program
- M.Tech. Modeling and Simulation – 2 Years (4 Semesters) – Full Time
- With specialization in following streams (minimum THREE specialization core courses and one year dissertation in the specialization stream need to be taken)
- Computational Chemistry (with School of Chemistry)
- Computational Material Science and Engineering (with School of Engineering Sciences & Technology)
- Computational Biology (with School of Life Sciences)
- Computational Physics (with School of Physics)
- Computational Science (with School of Computer and Information Sciences)
* This Programme is Approved by AICTE for the Complete Intake for the Year 2022-23
Program Educational Objectives (PEOs)
PEO-1: Produce Post graduates who can contribute to the Research & Development effectively
PEO-2: To provide students a deep insight into cutting edge technologies and tools.
PEO-3: To create globally competent technocrat’s with exposure to Scientific & Engineering aspects of development
PEO-4: To work collaboratively on multi-disciplinary projects and exhibit high levels of professional & ethical values
PEO-5: Create awareness of societal problems and its impact
Program Outcomes (POs)
PO-1: To independently carry out research/investigation and development work to solve practical problems
PO-2: To be able to write and present a substantial technical report/document
PO-3: To demonstrate knowledge and understanding of engineering principles and apply the same in solving the problems faced by society.
PO-4: To create, select, learn and apply appropriate techniques, resources, and advanced tools, including Modeling and prediction with an understanding of limitations
PO-5: To recognize the opportunities and contribute to collaborative-multidisciplinary scientific research to achieve common goals.
PO-6: To acquire professional and intellectual integrity, professional ethics code of conduct and understand the responsibility to contribute to the society for sustainable development
Program Specific Outcomes (PSOs)
PSO-1: Demonstrate comprehensive knowledge and skills in the fields of Computational Chemistry, Computational Material Science and Engineering, Computational Biology, Computational Physics and Computer Sciences
PSO-2: Use knowledge and skills in the fields of study and identify research questions that can be designed, modelled and simulated using computing paradigms and tested on HPC.
PSO-3: Apply disciplinary knowledge, transferable skills to design and develop solutions.
PSO-4: Communicate the results of studies undertaken in the respective fields of scientific journals and in popular science forums.
PSO-5: Demonstrate knowledge and transferable skills in the fields of study for employment the respective industry, government research institutes or agencies and journal publishers.
Programme Description
- Tech. Modeling and Simulation – 2 Years (4 Semesters) – Full Time
- With specialization in following streams (minimum THREE specialization core courses and one year dissertation in the specialization stream need to be taken)
- Computational Chemistry (with School of Chemistry)
- Computational Material Science and Engineering (with School of Engineering Sciences & Technology)
- Computational Biology (with School of Life Sciences)
- Computational Physics (with School of Physics)
- Computational Science (with School of Computer and Information Sciences)
This is a four-semester programme including two semesters of course work and two semesters of project work (Sem-III & IV). This programme is meant for students with some basic information about computing sciences, and well-versed with their fields to get specialization. Or else if they are well versed with computer science they can take Computational Science degree by studying advanced courses in Computer Sciences, that can be applied to solve grand challenging problems using HPC, ML&AI and Modeling and Simulations. Courses will fulfil student-centric learning needs. Students will be encouraged in design thinking and practical approaches to learning. Students will be made aware of real-life socio-economic problems for them to solve using HPC/AI technology learning
Why CMSD?
The programme’s objective is to make students ready to take up jobs in the industry and R&D institutions or to prepare them for higher studies in their domain of study. The course is designed in such a way to gives students the practical exposure and theoretical rigour equally in the domain knowledge they choose in the elective areas. The graduates of this programme should be exposed to emerging areas which require advanced expertise in computer literacy and computational techniques. The human resources generated from such efforts will be invaluable.
The resources of CMSD are uniquely suitable for this objective and should prove to be the ideal platform to take this initiative. The CMSD will coordinate and facilitate this programme’s administrative requirements and extend such support as is needed.
Participating Schools/Departments/Centres
I. Core Courses: Because of the heterogeneous nature of the students envisaged for this programme, and it is imperative that the minimum prerequisite knowledge base has to be provided to all students under the provision of core courses, to bring them to the same level playing field for further training.
It is possible that some subset of the contents of any such core courses may be already familiar to one or more stream of students, but not necessarily to all. Hence this core component, serving partly also the purpose of an academic prerequisite is a primary requirement.
Advanced training in emerging areas of applied Computer/Information Science is necessary for all the students aspiring this degree, cutting across the diverse domain expertise. All modern computational scientific research or development programmes require this skill set. So, a few courses with this objective are included in the core module.
II. Specialization Core Courses: Specialization core makes the core of the specialized area of study. A subset of these courses is mandatory for a student to earn a degree in that area of study. For those from other streams can also take Specialization core but will be treated as an elective, provided they are listed in elective basket from respective schools.
III. Electives: Design of the Elective Courses are left for the individual School and generally is expected to be in line with the programme’s objective. Additionally, few electives are added in the basket to see the present and future demand of the industry. There is a big pool common pool of electives. The centre will make all efforts so that these electives will be offered in different time slots, allowing students to opt and earn extra credits.
- Core Courses Credits: 33
- Specialization Core Courses (Three) Credits: 9 to 15
- Elective Course Credits: 3 to 5
- Project (III Semester 8 credits, IV Semester 12 credits): 20
- A Student need to complete minimum 70 credits to get the degree.
- Core and project credits are fixed, (33 + 20 = 53). Specialization core courses credits can be between 9 and 15. They have to choose elective(s) in such a way that total credits are minimum 70.
- Only core courses labs are mentioned here, other specialization core courses may have lab component that will be mentioned in (L-T-P) format.
Scheme
| I-Semester | ||||
| Code | Type | Course Title | L-T-P | Credits |
| M&S-101 | Core | Essential Mathematics for Modeling and Simulation | 3-1-0 | 4 |
| M&S-102 | Core | Data Structures and Algorithms | 4-0-0 | 4 |
| From respective Schools | Specialization Core | Specialization core-I | 3/4/5 | |
| M&S-103 | Core | Computer based Numerical and Statistical Techniques | 4-0-0 | 4 |
| M&S-104 | Core | Parallel Processing | 4-0-0 | 4 |
| M&S-Lab-I | Core | Parallel Processing Lab | 0-0-2 | 2 |
| M&S-Lab-II | Core | Programming Lab (C, Fortran, Python, Matlab) | 0-0-2 | 2 |
| M&S-105 | Core | Research Methodology and IPR | 1-0-0 | 1 |
| Total Credits: | 24 to 26 | |||
| II-Semester | ||||
| Code | Type | Course Title | L-T-P | Credits |
| M&S-201 | Core | Modeling and Simulation | 4-0-0 | 4 |
| M&S-202 | Core | Data Analytics and Visualization | 2-0-2 | 4 |
| From respective Schools | Specialization Core | Specialization core-II (from Stream only) | 3/4/5 | |
| From respective Schools | Specialization Core | Specialization core-III (from Stream only) | 3/4/5 | |
| Code of Elective | Elective | Elective | 3/4/5 | |
| M&S-Lab-III | Core | Modeling Simulation Lab | 0-0-2 | 2 |
| M&S-203 | Core | Mini project and Seminar | 2 | |
| Total Credits: | 21 to 26 | |||
| III-Semester | |||||
| Type | Course Title | Credits | |||
| Project | 8 | ||||
| IV-Semester | |||||
| Type | Course Title | Credits | |||
| Project | 12 | ||||
I. Specialization Core are to be offered by individual Schools to train the students in their area. In certain cases it can be from other schools also.
II. Three Specialization Core courses are mandatory for the student
III. Design of the Specialization Core is left for the individual School, and generally is expected to be in line with the objective of the programme.
IV. The CMSD will serve as the platform for any laboratory component planned as part of the electives.
V. Semester III and IV Project is to be done in the under the supervision of the faculty of the participating Schools, based on the domain knowledge needed for the project work.
VI. The CMSD can actively facilitate these computationally oriented projects in different ways, and coordinate academic administration connected with the assessment process.
VII. In the case of academically compelling circumstances, the students could be allowed to take up projects in duly approved institutions/industry with the participation of a supervising faculty member from the University.
Specialization | Specialization Core Courses | L-T-P | |
Computational Chemistry | Specialization core-I | CY403: Quantum Chemistry | 3-0-0 |
Specialization core-II | CY453: Molecular Spectroscopy | 3-0-0 | |
Specialization core-III | CY454: Statistical Thermodynamics | 3-0-0 | |
Computational Material Science and Engineering | Specialization core-I | MT411 Computational Materials Thermodynamics | 3-1-0 |
Specialization core-II | MT412 Computational Materials Thermodynamics Lab | 0-0-3 | |
Specialization core-III | MCP205 Computational Materials Science | 4-0-0 | |
Computational Biology | Specialization core-I | SB403: Bioinformatics | 3-0-2 |
Specialization core-II | SB451: Computational Systems Biology | 4-0-2 | |
Specialization core-III | SB454: Molecular Modeling and Simulations | 3-0-2 | |
Computational Physics | Specialization core-I | MCP103: Monte Carlo techniques and Molecular Dynamics | 4-0-0 |
Specialization core-II | MCP205: Computational Materials Science | 4-0-0 | |
Specialization core-III | MCP206: Network Science | 4-0-0 | |
Computational Science | Specialization core-I | CS401: Advanced Operating Systems | 4-0-0 |
Specialization core-II | CS351: Software Engineering | 3-0-2 | |
Specialization core-III | AI455: Machine Learning | 3-0-0 | |
Specialization | Electives (List may change based on the electives offered by Schools) | L-T-P | |
Computational Chemistry | Elective-I | CY575: Density Functional Theory | 2-0-0 |
Elective-II | CY577: Computational Chemistry | 2-0-0 | |
Elective-III | CYXXX: Introduction to Molecular Simulation Techniques | 2-0-0 | |
Elective-IV | CYXXX: AI, ML and Block chain in Chemistry | 2-0-0 | |
Computational Material Science and Engineering | Elective-I | CY575: Density Functional Theory | 2-0-0 |
Elective-II | CY577: Computational Chemistry | 2-0-0 | |
Elective-III | CYXXX: Introduction to Molecular Simulation Techniques | 2-0-0 | |
Elective-IV | CYXXX: AI, ML and Block chain in Chemistry | 2-0-0 | |
Computational Biology | Elective-I | Drug Design, Development and Delivery | 3-0-0 |
Elective-II | BT503: Emerging Technologies | ||
Elective-III | |||
Computational Physics | Elective-I | MCP203: Advanced courses in Computational Physics | 4-0-0 |
Elective-II | |||
Elective-III | |||
Computational Science | Elective-I | AI471 Colour Image Processing | |
Elective-II | CS493 Deep Learning | ||
Elective-III | AI474 Natural Language Processing | ||
Elective-IV | AI476 Rough Computing | ||
Elective-V | CS455 System Security | ||
Elective-VI | CS 476 Network Security | ||
Elective-VII | CS451 Virtualization | ||
Elective-VIII | CS472 Cloud Computing | ||
Elective-IX | CS481 Data Mining | ||
Elective-X | CS471 Wireless Sensor Networks | ||
Elective-XI | CS479 Advanced Computer Networks | ||
Elective-XII | IT472 Soft Computing | ||
Elective-XIII | CS480 Internet of things | ||