ES_APPM 495: Special Topics: Data Driven Methods for Dynamical Systems*

Quarter Offered

Winter : TTh 9:30-10:50 ; Mangan


Course description: The course will survey methods for characterizing time-series data by reading primary literature and implementing and testing methods on synthetic data. Students will simulate time-series from a variety of deterministic and stochastic systems and evaluate a range of methods. A goal of the course is to understand the suitability of different methods for characterizing systems with a different noise, nonlinearities, and other dynamic characteristics. Topics may include Taken's Embedding Theorem, Granger causality, entropy-based methods, sparse model selection, DMD, Koopman operators, and possibly Bayesian parameter estimation for time-series.  

* = approved course for undergraduate modeling requirement.