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.
@article{arxiv.2104.05106,
title = {Low-dimensional Flow Models from high-dimensional Flow data with Machine Learning and First Principles},
author = {Nan Deng and Luc R. Pastur and Bernd R. Noack},
journal= {arXiv preprint arXiv:2104.05106},
year = {2021}
}