Singapore–MIT Alliance
The High Performance Computation for Engineered Systems (HPCES) degree programme is the most technologically advanced and critically acclaimed computational engineering coursework available in the world today. Through a powerful combination of state-of-the-art distance learning technology and premiere academic collaboration, the HPCES programmes are graduating the very best high performance computation professionals.
High performance computation for engineered systems is a crucial component in the modeling, simulation, design, optimisation, control and visualisation of engineered systems in a wide range of technology and service industries. HPCES courses promote creativity as well as hands-on experience in an effort to study the improvement of both product and systems design. The programme's unified approach combines engineering science and systems optimization:
Engineering science
A keen focus on modeling and simulating physical phenomena and product behavior helps students to uncover shorter design cycles and improve functionality. Such virtual testing allows industries to design innovative, quality products with a minimum of costly physical prototypes.Systems optimisation
Careful attention to modeling and designing complex systems allow students to identify optimal configurations for maximum operational performance. The study of efficient process automation and integration is also emphasized. Such virtual design tools are widely used by industries to construct innovative solutions to complex tactical and strategic decisions.
The SMA academic programmes are also unique in their close affiliation with the IHPC, a premiere research institute in Singapore's Science Park. The IHPC specialises in research involving simulation and visualisation using advanced computational techniques. The Institute maintains close ties with the academia to undertake upstream research for the development of new technology, and at the same time supports local companies in industry-inspired research to enhance their capabilities and productivity.
|The Master of Science (S.M.) in HPCES
A professional master's degree programme that prepares graduates for careers
in simulation, design and optimisation for engineered systems. This one-year
programme focuses on the critical and effective application, modification,
and integration of existing simulation and optimisation software. In addition,
a two-week course of study at MIT is required.
The Doctor of Philosophy (Ph.D.) in HPCES
A research doctorate degree programme which emphasises the formulation,
analysis and implementation of new computational methods for the simulation
and optimisation of engineered systems for advanced technical careers in
research and development. Completion of the Ph.D. programme may require
three or more years. All Ph.D. students will have the opportunity to spend
a semester at MIT to take courses and conduct research with MIT students
and faculty.
|The SMA programme in HPCES is the first of its kind to deliver a unified perspective on simulation and optimisation techniques in the domains of engineering science and systems optimisation. Students learn to develop and apply advanced techniques for a diverse range of applications in:
Courses are primarily for people with an interest in, and passion for, modern and sophisticated high performance computation tools as the means to improve product and systems design. Careers might include employment in companies or research institutes in which modeling, simulation, design, and optimisation play a critical role. With a unified perspective on simulation and optimisation techniques, graduates are poised to accept high-level professional or research positions with thriving industries or entrepreneurial businesses around the globe.
|The S.M., M. Eng. and Ph.D. degree programmes contain the core curriculum, which includes the following courses:
SMA Project Course
Many of the courses will involve extensive hands-on experience. In addition
to the SMA core curriculum, the M. Eng. degree requires a Master's thesis,
and the Ph.D. degree
requires a Ph.D. thesis, as well as several additional advanced courses.
|Summer Session
SMA 5200 Numerical Linear Algebra (12 Units)
A two-week long intensive review of linear algebra with an emphasis on topics
related to numerical computation. The course meets for two hours of lectures
in the morning and a three-hour project lab in the afternoon. Topics covered
include a review of vectors, matrices, norms, range and null spaces, and
orthogonality, least squares problems and the QR algorithm, Gaussian elimination,
eigenvalues and eigenvectors, the singular value decomposition, and iterative
solution methods.
SMA 5202 Computing Technology and Tools (12 units)
A hands-on course on the technology and software tools for high performance
computation. Software packages used include Mathematica, C++ and/or Java,
Excel, PETSs, AVS, PVM and MINDSET.
Fall Session
SMA 5211 Introduction to Numerical Simulation (12 units)
An introduction to computational techniques for the simulation of a large
variety of engineering and engineered systems. Applications are drawn from
aerospace, mechanical, electrical, and chemical engineering, as well as
materials science and operations research. Topics include mathematical formulations;
network problems; sparse direct and iterative matrix solution techniques;
Newton iteration for nonlinear problems; solution techniques for eigenvalue
problems; discretisation methods for ordinary differential equations and
differential-alegebraic equations; discretisation methods for partial differential
and stochastic partial differential equations; methods for the solution
of integral equations; and Monte Carlo techniques and higher dimensional
problems.
SMA 5212 Numerical Methods for Partial Differential Equations
Covers the fundamentals of modern numerical techniques for a wide range
of linear and nonlinear elliptic, parabolic, and hyperbolic partial differential
and integral equations. Topics include: mathematical formulations; finite
difference, finite volume, finite element, and boundary element discretisation
methods; and direct and iterative solution techniques. The methodologies
described form the foundation for computational approaches to engineering
systems involving heat transfer, solid mechanics, fluid dynamics, and electromagnetics.
Computer assignments requiring programming.
|SMA 5213 Optimisation Methods (12 units)
This course is an introduction to the principal methods for linear, network,
discrete, nonlinear optimisation, as well as dynamic optimisation and optimal
control. Emphasis is on methodology and the underlying mathematical structures
and their connection to computational procedures.
Topics include:
SMA 5214 Numerical Algorithms on Advanced Computer Architectures (6 units)
The course introduces basic concepts of vector and parallel computer architectures
and their influence on numerical algorithms that are commonly used in the
numerical modeling of large-scale engineering simulation and optimisation
problems. Elements of MPI/PVM are covered to enable the implementation of
numerical algorithms written in FORTRAN and C/C++ on parallel computers.
Hands-on parallel computation exercises on selected numerical algorithms
are included to give insight into performance measures and the influence
of computer architectures on the programming and performance of selected
numerical algorithms.
SMA 5215 Integrated Simulation and Optimisation of Engineered Systems (6
units fall/6 units spring)
An integrated presentation of simulation and optimisation techniques for
engineered systems. Emphasis is on understanding and exploiting underlying
mathematical frameworks and associated computational methodologies common
to both simulation and optimisation; and on the systematic application of
optimisation methods to problems for which the forward analysis requires
extensive simulation. Particular topics include optimisation formulations
of unconstrained and constrained equilibrium problems; methods for unconstrained
and constrained optimal control of systems governed by ordinary differential
equations; formulation, approximation, and solution of inverse and parameter
estimation problems; and optimisation of systems described or simulated
by statistical or stochastic simulation techniques. Applications will be
drawn from the engineering, operations research, and management and finance
domains.
|Spring Session
SMA 5212 Numerical Methods for Partial Differential Equations (12 units)
A presentation of the fundamentals of modern numerical techniques for a
wide range of linear and nonlinear elliptic, parabolic and hyperbolic partial
differential equations and integral equations central to a wide variety
of applications in science, engineering, and other fields.
Topics include:
SMA 5216 Integrated Simulation and Optimisation of Engineered Systems Part
II
An integrated presentation of simulation and optimisation techniques for
engineered systems. Emphasis is on understanding and exploiting underlying
mathematical frameworks and associated computational methodologies common
to both simulation and optimisation; and on the systematic application of
optimisation methods to problems for which the forward analysis requires
extensive simulation. Particular topics include optimisation formulations
of unconstrained and constrained equilibrium problems; methods for unconstrained
and constrained optimal control of systems governed by ordinary differential
equations; formulation, approximation, and solution of inverse and parameter
estimation problems; and optimisation of systems described or simulated
by statistical or stochastic simulation techniques. Applications will be
drawn from the engineering, operations research, and management and finance
domains.
SMA 5223 Systems Optimisation: Models and Computation
An applications-oriented course on the modeling of large-scale systems in
decision-making domains and the optimisation of such systems using state-of-the-art
optimisation tools. Application domains include: transportation and logistics
planning, pattern classification and image processing, data mining, design
of structures, scheduling in large systems, supply-chain management, financial
engineering, and telecommunications systems planning. Modeling tools and
techniques include linear, network, discrete and nonlinear optimization,
heuristic methods, sensitivity and post-optimality analysis, decomposition
methods for large-scale systems, and stochastic optimization.
SMA Project Class
Capstone subject providing hands-on technical skills and an opportunity
for group project work and technical communication.
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