Imperial College London
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Luca Magri

Faculty of Engineering, Department of Aeronautics

Professor of Scientific Machine Learning
 
 
 
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Contact

 

l.magri Website

 
 
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Location

 

CAGB324City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Doan:2021,
author = {Doan, NAK and Polifke, W and Magri, L},
title = {Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach},
url = {http://arxiv.org/abs/2102.07514v1},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We propose a physics-constrained machine learning method-based on reservoir computing- to time-accurately predict extreme events and long-term velocity statistics in a model of turbulent shear flow. The method leverages the strengths of two different approaches: empirical modelling based on reservoir computing, which it learns the chaotic dynamics from data only, and physical modelling based on conservation laws, which extrapolates the dynamics when training data becomes unavailable. We show that the combination of the two approaches is able to accurately reproduce the velocity statistics and to predict the occurrence and amplitude of extreme events in a model of self-sustaining process in turbulence. In this flow, the extreme events are abrupt transitions from turbulent to quasi-laminar states, which are deterministic phenomena that cannot be traditionally predicted because of chaos. Furthermore, the physics-constrained machine learning method is shown to be robust with respect to noise. This work opens up new possibilities for synergistically enhancing data-driven methods with physical knowledge for the time-accurate prediction of chaotic flows.
AU  - Doan,NAK
AU  - Polifke,W
AU  - Magri,L
PY  - 2021///
TI  - Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach
UR  - http://arxiv.org/abs/2102.07514v1
ER  -
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