Low-dimensional Flow Models from high-dimensional Flow data with Machine Learning and First Principles

Nan Deng; Luc R. Pastur; Bernd R. Noack

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arXiv · 2104.05106

Low-dimensional Flow Models from high-dimensional Flow data with Machine Learning and First Principles

Nan Deng·Luc R. Pastur·Bernd R. Noack

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Abstract

Reduced-order modelling and system identification can help us figure out the elementary degrees of freedom and the underlying mechanisms from the high-dimensional and nonlinear dynamics of fluid flow. Machine learning has brought new opportunities to these two processes and is revolutionising traditional methods. We show a framework to obtain a sparse human-interpretable model from complex high-dimensional data using machine learning and first principles.

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Nan Deng, Luc R. Pastur, Bernd R. Noack. 2021-04-11. Low-dimensional Flow Models from high-dimensional Flow data with Machine Learning and First Principles. https://arxiv.org/abs/2104.05106

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