Model Reduction

4 credits

Students will learn the mathematical tools, theory, and algorithms of model reduction so as to reproduce and predict behaviors in complex systems with low-dimensional models. Covers a wide range of methods and theory: system-theoretic (balanced truncation, transfer-function interpolation), data-driven and projection-based (proper orthogonal decomposition), and model learning approaches (dynamic mode decomposition, operator inference, Loewner-based system identification, eigensystem realization). Prerequisites: MAE 280A and MAE 290A.