AEM8453: Model Reduction and Approximation of Dynamical Systems

3 CreditsGoal 10 - People/EnvironmentGoal 6 - Hum: Arts/Lit/PhilOnline 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
SFDCBA
  • 4.39

    /5

    Recommend
  • 4.21

    /5

    Effort
  • 4.45

    /5

    Understanding
  • 4.39

    /5

    Interesting
  • 4.13

    /5

    Activities


  • Samyok Nepal

    Website/Infrastructure Lead

  • Kanishk Kacholia

    Backend/Data Lead

  • Joey McIndoo

    Feature Engineering

Contribute on our Github

Gopher Grades is maintained by Social Coding with data from Summer 2017 to Fall 2024 provided by the Office of Institutional Data and Research

Privacy Policy