The University of Texas at Austin Cockrell School of Engineering 45 courses 122 total credits
Computational engineering is a relatively new field in engineering that recognizes the increasing demand for advanced computational methods in engineering practice. Computational engineering in this context refers to the study and development of computer algorithms that translate mathematical and physical descriptions of engineering problems into languages and software that computers can process. This emphasis distinguishes computational engineering from computer science and computer engineering. Computational engineers must have basic knowledge of fundamental engineering and science, with more advanced knowledge of mathematics, algorithms and software engineering and design. Because of their extensive education in these disciplines, computational engineers can work in a variety of areas. The objectives of the computational engineering degree program are to prepare students for professional practice in engineering; to prepare students for such post-baccalaureate study as their aptitudes and professional goals may dictate; to instill in students a commitment to acquire and apply new knowledge and to ethical behavior throughout their professional careers; and to make students aware of the global and societal effects of technology. To meet these objectives, the faculty has designed a rigorous curriculum that emphasizes fundamentals in the basic sciences and the humanities, integrates classroom and laboratory experiences in engineering, with advanced instruction in mathematics, statistics and computational science. The curriculum requires students to use modern engineering tools and computer technology, to work individually, and to practice teamwork. The initial coursework in the computational engineering curriculum emphasize fundamental material along with engineering sciences, while the later coursework goes into further depth in mathematics, algorithms, computer languages, software engineering and design, and experimentation. The major offers technical electives in the third and fourth years where students may choose from a variety of courses that orient them towards different engineering applications and better prepare those students who may choose to pursue a graduate degree.
| Free electives: Additional coursework to reach total hours required. | 0 credits |
| Foreign Language other than English, Beginning Proficiency | credits |
| College Requirements - Engineering | credits |
| General University Requirements | credits |
Degree
Degree
Approved technical electives
| Code | Course Title | Credits |
|---|---|---|
| E S 333T | Engineering Communication | 3 |
| RHE 306 | Rhetoric and Writing | 3 |
| Total Degree | 12 credits | |
Math/Computational Elective
| Code | Course Title | Credits |
|---|---|---|
| M 325K | Discrete Mathematics | 3 |
| M 340L | Matrices and Matrix Calculations | 3 |
| M 368K | Numerical Methods for Applications | 3 |
| SDS 322E | Elements of Data Science | 3 |
| SDS 324E | Elements of Regression Analysis | 3 |
| Total Math/Computational Elective | 3 credits | |
Foundational Elective
| Code | Course Title | Credits |
|---|---|---|
| ASE 362K | Compressible Flow | 3 |
| ASE 366K | Spacecraft Dynamics | 3 |
| ASE 479W | Aerial Robotics | 4 |
| M E 344 | Dynamic Systems and Controls | 3 |
| RBT 350 | Gateway to Robotics | 3 |
| Total Foundational Elective | 3 credits | |
Advanced Elective or Math/Computational Elective
| Code | Course Title | Credits |
|---|---|---|
| COE 379L.1 | Introduction to Machine Learning and Data Sciences | |
| COE 379L.2 | Simulation-based Aerodynamic Design and Analysis | |
| COE 379L.3 | Computational Methods | |
| COE 379L.6 | Software Design for Responsible Intelligent Systems | |
| M 361K | Introduction to Real Analysis | 3 |
| Total Advanced Elective or Math/Computational Elective | 6 credits | |
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