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

Year: 2021

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2104.05106

Authors: Deng, Nan, Pastur, Luc R., Noack, Bernd R.

Institutions: Laboratoire Interdisciplinaire des Sciences du Numérique, Institut des Sciences de la Mécanique et Applications Industrielles, Harbin Institute of Technology

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3046578392

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