- Schedule of Classes - June 20, 2021 7:14PM EDT
- Course Catalog - June 20, 2021 7:15PM EDT
Course information provided by the Courses of Study 2020-2021. Courses of Study 2021-2022 is scheduled to publish by July 1.
Course covers a variety of ways in which models and experimental data can be used to estimate model quantities that are not directly measured. Covers methods for solving the class of inverse problems that take the following form: given partial information about a system, what is the behavior of the whole system? Main estimation methods presented are batch least-squares-type estimation for general problems and Kalman filtering for dynamic system problems. Course deals with the issue of observability, which amounts to a consideration of whether a given inverse problem has a unique solution, and briefly covers the concept of statistical hypothesis testing. Techniques for linear and nonlinear models are taught. Both theory and application are presented.
When Offered Spring.
Permission Note Enrollment limited to: MS and Ph.D. students or permission of instructor.
Prerequisites/Corequisites Prerequisite: linear algebra, differential equations, undergraduate-level probability theory, MATLAB programming. Prerequisite or corequisite: MAE 4780/MAE 5780 or ECE 5210.
Regular Academic Session.
Credits and Grading Basis
4 Credits Graded(Letter grades only)
Class Number & Section Details
- MWTo Be Assigned
- Aug 26 - Dec 7, 2021
Instruction Mode: Planned for In Person
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