Artificial Intelligence, Modeling and Simulation in Engineering (AIMS) Certificate Graduate Program
Artificial intelligence (AI) is a technology that mimics human intelligence to solve complex problems and perform complex tasks. Machine learning (ML) is a subfield of AI that uses statistical methods to learn from data without being explicitly programmed. Deep learning (DL) is one of the main subsets of ML and uses multi-layered neural networks to learn from data. Modeling and simulation (M&S) in engineering is a field that uses mathematical models as a basis for simulations to generate data analyzed for product and system design. M&S is a knowledge-based approach that develops models to generate data, while ML is a data-based approach that learns from data to generate models. The AIMS certificate program aims to prepare College of Engineering students with new modeling strategies that combine the approaches of AI/ML/DL and M&S to bridge the knowledge gap between them and take advantage of respective approaches in conjunction with UQ that accounts for both model and data uncertainties, allowing for analysis and design of complex products and systems.
The AIMS graduate certificate program requires a minimum of 15 semester hours (s.h.) of the following graduate coursework. To earn the certificate, the student is required to attain a minimum GPA of 3.00 in coursework specifically for the certificate.
| Course number | Course title | Credit | Offering |
|---|---|---|---|
| ME:5170 | Data-driven Analysis in Engineering Mechanics | 3 s.h. | Fall, odd years |
| ME:5300 | Uncertainty Quantification and Design Optimization | 3 s.h. | Fall, even years |
| ME:6265* | Multiscale Computational Science and Engineering | 3 s.h. | Spring, every 2 years |
*Previously ME:6255 Multiscale Modeling
| Course number | Course title | Credit | Offering |
|---|---|---|---|
| ME:4117 | Finite Element Analysis | 3 s.h. | Every Fall |
| ME:4150 | Artificial Intelligence in Engineering | 3 s.h. | Every Fall |
| ME:4175 | Computational Naval Hydrodynamics | 3 s.h. | Spring, every 2 years |
| ME:5143 | Computational Fluid and Thermal Engineering | 3 s.h. | Every Fall |
| ME:6240 | Probabilistic Inference & Estimation for Mechanical Systems | 3 s.h. | Spring, odd years |
| ME:7256 | Computational Solid Mechanics | 3 s.h. | Spring, odd years |
| ME:7257 | Probabilistic Mechanics and Reliability | 3 s.h. | Fall, every 3 years |
| ME:7269 | Computational Fluid Dynamics & Heat Transfer | 3 s.h. | Spring, every 3 years |
One elective course may be selected from:
- Engineering courses at an upper level (e.g., ME courses numbered 4100 and above),
- Mathematics, physics, or chemistry courses at a more advanced level than those required in the ME curriculum, or
- Independent investigation in a mechanical engineering subject.
Students could petition to substitute the courses listed in Table 2 with graduate-level AI/ML/DL and M&S related courses offered by ME and other departments.
Students are strongly encouraged to participate in at least one workshop, related to Python, R, or high performance and parallel computing offered by Information Technology Services Research Services (ITS-RS), and HACKUIOWA.
Students who have one or more courses yet to complete when the certificate program is applied are allowed to count courses completed in previous recent semesters toward their certificate. This policy applies to ME:6255 Multiscale Modeling.
Application link and more information
To apply, submit an application online.
For further information, contact Professor Jia Lu
