Modelling of physical systems with a Hopf bifurcation using mechanistic models and machine learning
Modelling of physical systems with a Hopf bifurcation using mechanistic models and machine learning
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使用机械模型和机器学习对具有 Hopf 分岔的物理系统进行建模
DOI:
10.1016/j.ymssp.2023.110173
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发表时间:
2023
影响因子:
8.4
通讯作者:
Lee K
中科院分区:
文献类型:
--
作者:
Lee K
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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DOI:
--
发表时间:
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期刊:
--
影响因子:
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影响因子:
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DOI:
10.21203/rs.3.rs-55125/v1
发表时间:
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期刊:
ArXiv
影响因子:
--
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