Applied Math And Analysis Seminar
Tuesday, November 8, 2022, 3:15pm, Physics 119
Di Qi (Purdue)
Statistical reduced-order models and data-driven closure strategies for turbulent systems
Abstract:- The capability of using imperfect statistical reduced-order models to capture crucial statistics in turbulent systems is investigated. Much simpler and more tractable block-diagonal models are proposed to approximate the complex and high-dimensional turbulent dynamical equations through parametric closure models. New machine learning strategies are proposed to learn the expensive unresolved processes directly from data. A systematic framework of correcting model errors with empirical information theory is introduced, and optimal model parameters under this unbiased information measure can be achieved in a training phase before the prediction. It is demonstrated that crucial principal statistical quantities in the most important large scales can be captured efficiently with accuracy using the reduced-order model in various dynamical regimes of the flow field with distinct statistical structures.
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