Concentrations: Biomedical || Energy || Engineering Computation || Environment & Sustainability || Manufacturing Design || Materials || Process Data Analytics
Course 10-ENG: Bachelor of Science in Engineering with Concentration
Engineering Computation
Computation has become an increasingly important tool in engineering. Today computational techniques are more effective and less expensive than experiments for the solution of many engineering problems, and are useful complements to experiments for most of the remaining problems. Computation is commonly used to provide insights that go beyond purely experimental studies. This concentration covers fundamental concepts, techniques, tools, and applications of engineering computation.
Note: Students can get the CS minor by taking four additional courses (for details, see https://www.eecs.mit.edu/academics/undergraduate-programs/minor-in-computer-science/)
Students in this concentration are required to take four subjects from four categories. Although not required, students are encouraged to take
10.34 Numerical Methods Applied to Chemical Engineering
as one of their technical electives.
A. Introduction to Programming (select at least one)
| Number | Name | Units | GIR | Prerequisites |
| 6.100A | Introduction to Computer Science Programming in Python | 6 | ||
| 6.100L | Introduction to Computer Science and Programming | 9 |
B. Computational Mathematics (select at least one)
| Number | Name | Units | GIR | Prerequisites |
| 6.1200[J] | Mathematics for Computer Science | 12 | REST | Calculus I (GIR) |
| 18.200/18.200A | Principles of Discrete Applied Mathematics | 15/12 |
C. Introduction to Applications (select at least one)
| Number | Name | Units | GIR | Prerequisites |
| 6.9080 | Introduction to EECS via Robotics | 12 | LAB | 6.100A or permission of instructor |
| 6.3400 | Introduction to EECS via Communications Networks | 12 | LAB | 6.100A |
| 6.4900 | Introduction to EECS via Medical Technology | 12 | LAB | Calculus II (GIR) and Physics II (GIR) |
| 6.9010 | Introduction to EECS via Interconnected Embedded Systems | 12 | LAB | |
| 6.C01 + 10.C01 | Modeling with Machine Learning: from Algorithms to Applications; Machine Learning for Molecular Engineering | 12 | Calculus II (GIR) and 6.100A |
D. Computational Engineering (select at least one)
| Number | Name | Units | GIR | Prerequisites |
| 2.086 | Numerical Computation for Mechanical Engineers | 12 | REST | Calculus II (GIR) and Physics I (GIR) |
| 6.4100 | Artificial Intelligence | 12 | 6.100A | |
| 10.437[J] | Computational Chemistry | 12 | ||
| 18.085 | Computational Science and Engineering I | 12 | Calculus II (GIR) and (18.03 or 18.032) | |
| 18.086 | Computational Science and Engineering II | 12 | Calculus II (GIR) and (18.03 or 18.032) |
Approved Graduate Subject Alternatives
| Number | Name | Units | GIR | Prerequisites |
| 2.097[J] | Numerical Methods for Partial Differential Equations | 12 | 18.03 or 18.06 | |
| 10.34 | Numerical Methods Applied to Chemical Engineering | 9 | Permission of instructor |