Selected Topics in Mechanical Engg (495-0-36)
Topic
Computational Intelligence for Engineering
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 L160: Tues, Thurs 9:30AM - 10:50AM
Overview of class
Applied Computational Intelligence for Engineering
This course will introduce students to computational intelligent tools that train, calibrate, solve, optimize, and enable differentiable simulations through deep learning-based computations for large-scale scientific and engineering problems. Materials, manufacturing, and multiphysics problems will be discussed and demonstrated, showing how high-dimensional problems in these fields can be reformulated using the new paradigm of statistical deep-learning- and physics-based data-driven computations and simulations.
Project: Students must complete a final project. The proposal is due in the 5th week.
Homework: Four computer assignments related to the subject materials will be given.
Grading: Reading assignments (15%) + Homework (HW) (30%) + Midterm Project Proposal (20%) + Final project presentation and report (35%)
•Each HW will involve a theoretical analysis and computer implementation. HW will be announced at the beginning of the module, and the relevant concepts will be discussed in class.
•The reading assignments will be designed to expose the students to the broader application of the methods and some necessary materials for comprehension of the lectures. These will be in written report format.
•Final project: students are encouraged to propose a project based on their research using the methods taught in this course. Students without research projects will be given ideas for projects.
Registration Requirements
Graduate students and senior undergraduate students with a background in applied mathematics and an interest in data science applications.
Prerequisites: Multivariate calculus, MATLAB, introductory knowledge of Python programming.
Evaluation Method
Homework (HW) (30%)
Reading assignments (15%)
Midterm Project Proposal (20%)
Final project presentation and report (35%)
Class Materials (Required)
None