Special Topics in Mechanical Engineering (395-0-1)
Topic
Deep Learning Discrete Calculus for Engineering Ap
Instructors
Wing K Liu
847/491-7094
Technological Institute, Rm A326, 2145 Sheridan Rd, EV CAMPUS
Office Hours: w-liu@northwestern.edu
Meeting Info
Technological Institute LG52: Tues, Thurs 9:30AM - 10:50AM
Overview of class
Deep-Learning Discrete Calculus (DLDC) is an emerging field that integrates calculus, numerical methods, machine, and deep learning algorithms to identify, model, and solve mathematical science systems governed by differential equations with uncertain parameters. In this class, student will learn to generate the essential data, apply the deep learning discretization of ordinary and partial differential equations, and reveal the unknown governing parameters and solutions of a variety of daily life and engineering problems.
Registration Requirements
Students need to know multivariate calculus, MATLAB and have an introductory knowledge on Python ProgrammingStudents need to know multivariate calculus, MATLAB and have an introductory knowledge on Python Programming.
Evaluation Method
Computer assignments (4×10%)
Presentation Proposal 20% (due 5th week of quarter)
Final Project Presentation and Report 40%
Class Materials (Suggested)
Helpful Readings: Need more references for the applications and case studies.
1. Tutorial on PyTorch: https://www.youtube.com/watch?v=IC0_FRiX-sw&ab_channel=PyTorch
2. Tutorial on Python: https://www.youtube.com/watch?v=kqtD5dpn9C8&ab_channel=ProgrammingwithMosh
3. Kong, Q., Siauw, T. and Bayen, A., 2020. Python Programming and Numerical Methods: A Guide for Engineers and Scientists. Academic Press. (Free Online: https://pythonnumericalmethods.berkeley.edu/notebooks/Index.html)
4. Liu, W.K., Gan, Z. and Fleming, M., 2021. Mechanistic Data Science for STEM Education and Applications. Springer. ISBN 978-3030878313