Uncertainty quantification of turbulent systems via physically consistent and data-informed reduced-order models

Uncertainty quantification of turbulent systems via physically consistent and data-informed reduced-order models

<jats:p> This work presents a data-driven, energy-conserving closure method for the coarse-scale evolution of the mean and covariance of turbulent systems. Spatiotemporally non-local neural networks are employed for calculating the impact of non-Gaussian effects to the low-order statistics of...

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Bibliographic Details
Main Authors:	Charalampopoulos, A, Sapsis, T
Other Authors:	Massachusetts Institute of Technology. Department of Mechanical Engineering
Format:	Article
Language:	English
Published:	AIP Publishing 2022
Online Access:	https://hdl.handle.net/1721.1/145499

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