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arXiv 2209.06910math.DScs.LGnlin.CD
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Modelling of physical systems with a Hopf bifurcation using mechanistic models and machine learning

University of Bristol(布里斯托大学);Imperial College London(伦敦帝国学院)

K. H. Lee, D. A. W. Barton, L. Renson

英文摘要:

We propose a new hybrid modelling approach that combines a mechanistic model with a machine-learnt model to predict the limit cycle oscillations of physical systems with a Hopf bifurcation. The mechanistic model is an ordinary differential equation normal-form model capturing the bifurcation structure of the system. A data-driven mapping from this model to the experimental observations is then identified based on experimental data using machine learning techniques. The proposed method is first demonstrated numerically on a Van der Pol oscillator and a three-degree-of-freedom aeroelastic model. It is then applied to model the behaviour of a physical aeroelastic structure exhibiting limit cycle oscillations during wind tunnel tests. The method is shown to be general, data-efficient and to offer good accuracy without any prior knowledge about the system other than its bifurcation structure.

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