Introduces data-driven system identification methods for dynamical modeling of atmospheric, ocean, and robotic vehicles from experimental data. Topics include experiment design; model structure selection; parameter and state estimation; regression-based and maximum-likelihood approaches; and data analysis in both time and frequency domains. Emphasis is placed on integrating physical modeling with data-driven techniques and on assessing model validity and uncertainty.
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University of California, Santa Cruz
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