AEM 8453: Model Reduction and Approximation of Dynamical Systems

3 CreditsOnline Available

In this course, we will study analytical and data-driven methods for model reduction and approximation of dynamical systems. The focus will be on learning the relevant mathematics and tools for obtaining “lean” low-dimensional representations of dynamical systems, which can be used to facilitate analysis and design. Roughly half of the course will be devoted to the problem of model reduction: i.e., given a mathematical description of a system, reduce the number of degrees of freedom required to faithfully represent that system. The other half of the course will be devoted to data-driven approximation of dynamical systems: i.e., given empirical data generated by a dynamical system, determine a mathematical representation for the underlying system dynamics. Although these two general problems are distinct, they are closely related and will be studied in parallel throughout the term.

View on University Catalog

All Instructors

A- Average (3.795)Most Common: A (40%)

This total also includes data from semesters with unknown instructors.

15 students
  • 4.39


  • 4.21


  • 4.45


  • 4.39


  • 4.13



      Contribute on our Github

      Gopher Grades is maintained by Social Coding with data from Summer 2017 to Fall 2023 provided by the Office of Institutional Data and Research

      Privacy Policy