ICE Graduate Certificate Program
The objective of the certificate program is to train students in the area of complex multiscale multi-disciplinary problems and their resolution by computation. The certificate program is an important component of the educational mission of the newly formed college of Engineering Institute for Computational Engineering (ICE).
Computational Science and Engineering (CSE) as a field has blossomed over the past two decades. The conventional educational paradigm in CSE brings together science and engineering disciplines and computer science to enable large-scale scientific simulations on modern parallel machines. In this approach the focus is primarily on modeling, numerical methodology and high performance computing. Issues pertaining to verification and validation (V&V) and uncertainty quantification (UQ) are limited to simple comparison with experimental data and plotting of error bars. Risk analysis and decision making are not integrated into the planning process.
The new certificate program will go beyond the conventional CSE paradigm to allow modern engineers and scientists to simultaneously
- Learn how to effectively perform large-scale scientific simulations,
- Manage and analyze voluminous datasets,
- Identify key quantities of ultimate significance to final decision,
- Reduce the voluminous data to focus on these key quantities,
- Rigorously establish uncertainties in the prediction of the key quantities, and
- Evaluate probabilistic risks and rewards involved in the final decision making.
Towards this goal the certificate will integrate data mining, high-performance computing, V&V, UQ and decision making with physical disciplines. Thus, mathematical models, numerical methodology, and high-performance computing techniques to be employed are iteratively decided on the basis of validation, uncertainty quantification, and uncertainty reduction of the key outcome quantities to be used for final decision making. The significance of this certificate program is the integration of (i) science and engineering disciplines, (ii) verification, validation, uncertainty reduction, and uncertainty quantification and (iii) risk analysis, decision making and diffusion of knowledge.
Who can enroll?
All the schools and departments within the college of engineering are active participants of ICE. Any graduate student pursuing Masters or PhD degree in one of the engineering disciplines at the University of Florida can enroll in Graduate Certificate in Scientific Computing. Graduate students in related science disciplines can also enroll in the certificate program. The certificate program is intended to complement the graduate degree each student will receive from his or her home department.
Non-degree seeking students may pursue Graduate Certificate in Scientific Computing with approval from ICE administrative committee, as long as the applicant has at least a bachelor’s degree in engineering or physical sciences, or equivalent from a regionally accredited institution.
Interested degree-seeking and non-degree-seeking students must fill out the enrollment form, which can be accessed through ICE, college and departmental websites.
- Student must enroll and admitted to the certificate program
- Student must complete 9 credits of ICE approved courses
- 6 of these credits must be from the core courses (see below) and the other 3 credits can be from the list of approved courses (see Table below)
- Student must obtain B or better in each of the three courses
- Students are encouraged to attend ICE seminars
- Students are encouraged to participate in the annual ICE symposium
CIS 6930 High performance and data intensive computing:
Issues pertaining to massively parallel computing, load balancing, parallel I/O, fault tolerance, and parallel visualization will play an important role. In addition, there are issues that pertain to management and processing of large databases, such as data compression, data mining and machine learning. The computational cost and challenges are far greater in the new paradigm. For example, uncertainty quantification requires multiple repetitions of the simulations and experiments for different values of the uncertain parameters. The overall computational effort often requires the use of terascale to petascale architectures. Another issue that we increasingly face is harnessing the performance of extant and emerging computer technologies, such as multi-core chips and Graphical Processing Units (GPUs).
EGM 6934: Verification, validation, uncertainty reduction, uncertainty quantification (VVUU):
For simulations to be truly predictive, we must ensure that the physical models are appropriate, that the computer code correctly implements the numerical methods, and that the sources of uncertainties are identified and quantified. Similarly, if the scientific database is being generated from experiments, a clear understanding of all the experimental uncertainties and measurement errors must be quantified. The various uncertainties and numerical errors must be propagated to quantify the overall uncertainty in the output. In order to establish meaningful validation, it is essential to identify the biggest contributors uncertainties and to systematically reduce the level of uncertainties and errors. Through a rigorous iterative validation process of comparing against experimental measurements, the simulation framework can be used in a true “pre-diction” mode.
|Approved List of Non-core Courses|
|CAP 5805||Computer Simulation Concepts|
|CDA 6156||High Performance Computer Architechture|
|CEG 6505||Numerical Methods of Geomechanics|
|CEN 6070||Software Testing and Verification|
|CEN 5035||Software Engineering|
|CES 5010||Probabilistic and Stochastic Methods in Civil Engineering|
|CES 6165||Computer Methods in Civil Engineering|
|CHM 6303||Methods in Computational Biochemistry and Structural Biology|
|CIS 6930||Parallel Algorithms|
|COP 5618||Concurrent Programming|
|COP 5555||Programming Language Principles|
|COT 4501||Numerical Analysis: A Computational Approach|
|EEE 6428||Computational Nanoelectronics|
|EEL 6763||Parallel Computer Architecture|
|EGM 6341||Numerical Methods of Engineering Analysis|
|EGM 6342||Computational Fluid Dynamics|
|EEL 6533||Statistical Decision Theory|
|EEL 6935||Computer Simulation of Integrated Circuits and Devices|
|EEL 6935||Distributed Computing|
|EEL 6785||High Performance Computer Networks|
|EGM 6934||Data Measurement and Analysis|
|EGM 6352||Advanced Finite Element Analysis|
|EGM 7819||Advanced Topics in Computational Fluid Dynamics|
|EMA 6804||Quantum Methods in Computational Materials Science|
|EMA 6808||Error Analysis and Optimization|
|EMA 6803||Classical Methods in Computational Material Science|
|EML 5526||Finite Element Analysis|
|EOC 6850||Numerical Simulation Techniques in Coastal and Ocean Engineering|
|ESI 6529||Digital Simulation Techniques|
|ESI 6546||Stochastic Modeling and Analysis|
ICE Graduate Fellowship
The institute will award 4 Tuckett ICE Fellowships to qualified graduate students. These awards are being funded by the Tuckett Fellowship Endowment. Each fellowship is for four years and provides the recipient $5,000 annually ($20,000 over four years) with an additional $3,000 to be used for travel expenses during this four year time span.
The Tuckett ICE Fellowship recipient must satisfy the following requirements:
- Must enroll and complete the ICE graduate certificate program
- Must be an actively enrolled advanced degree student in the college of engineering
- Must be engaged in collaborator research in the area of computational science and engineering
Each year all the departments in the college of engineering will be encouraged to submit applications for these fellowships to the Associate Dean of Academic Affairs at the college of engineering, who will forward them to the Institute Director. The director will work with the faculty advisory committee to select the most deserving applicants. A detailed description of the selection process will be distributed. The objective of this fellowship program is to support initiation of inter-disciplinary computational research on campus and to attract and train students in state-of-the-art scientific computation.
Institute Fellowship is also available in the following options:
- Florida Institute for National Security (FINS)
- Institute for Computational Engineering (ICE)
- Institute for Cell & Tissue Science and Engineering (ICTSE)
- Institute for Networked Autonomous Systems (INAS)
- Nanoscience Institute for Medical & Engineering Technology (NIMET)
Fellowship Application Form
Nomination can only be made by faculty members. Gatorlink account login is required for nomination.