Numerous technical fields have increasingly acknowledged the need for cross-functional collaboration in design and implementation. For example, aerospace engineering often requires the combination of several disciplines, such as fluids, structures, and system controls. The interaction between these disciplines can be complex, creating challenges to design optimization. This course will cover the mathematical and algorithmic fundamentals of optimization, including derivative and derivative-free approaches for both linear and non-linear problems. Special emphasis is placed on multidisciplinary design optimization. Design applications range from aircraft to automated vehicles.
Students taking this course for 4 units will be expected to spend 30 additional hours on the project and course paper.
This course is cross-listed as CS361.
- Mykel Kochenderfer Assistant Professor, Aeronautics & Astronautics
- Globally optimizing complex, high-dimensional, multimodal objectives
- Population methods including genetic algorithms and particle swarm optimization
- Handling uncertainty when optimizing non-deterministic objectives
- Principled methods for optimization when design iterations are expensive
Note on Course Availability
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.
3.0 - 4.0
Some familiarity with probability, programming and multivariable calculus.