ASE3001: Computation Lab.

Course Info.

Course descriptions

• This course covers elementary computational techniques for solving mathematical problems in aerospace engineering and other engineering disciplines. Students will use high level programming languages to formulate, interpret and analyze practical real-world problems encountered at a wide variety of engineering disciplines. Covered topics include but not limited to differential equations, linear algebra, probability, Fourier transform, introductory machine learning and artificial intelligence.

Instructors

• Instructor: Prof. Jong-Han Kim (Rm.507)

• Head TA: Seungyeop Lee (Rm.502)

• TA: Hyejin Park (Rm.502)

Lectures

• Wed 14:30-17:30 (Rm.216)

Office hours

• JHK: Tue/Thr 16:00-17:00 (Rm.507), or by appointments.

• TAs: By appointments.

Prerequisites

• Previous exposure to programming languages (Python or others).

Reference textbooks

• There are no required textbooks.

Grading policy

• Final exam (40%)

• Midterm exam (30%)

• Homework assignments and class participation (30%)

Lecture Notes

The link directs to the associated Jupyter notebook, which opens on Google Colaboratory when the “Open in Colab” button is clicked.

1. A very short Python review with Numpy, Matplotlib, and Pandas modules (Files: kfxsim.csv)

2. Differential equations and dynamical systems

3. Image processing

4. Monte-Carlo methods

5. Discrete Fourier transform

6. Bayesian inference

7. Signal processing

8. Optimization

9. Control design

10. Rocket guidance

11. Machine learning

12. Deep learning

Assignments

Assignments will be up with the lab session, during which the students start to work on them. Completed works should be turned in by next week's lecture to the course TAs.

1. Warm-up data exploration and visualization (due 9/10)

2. Numerical simulation (due 9/17)

3. Image filtering (due 9/24)

Exams

1. Midterm exam (2020 Autumn) (solution)

2. Final exam (2020 Autumn)