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
Lee K
中科院分区:
工程技术1区
文献类型:
--
作者:
Lee K

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我们提出了一种新的混合建模方法,结合了机械模型与机器学习模型来预测具有Hopf分岔的物理系统的极限环振荡。机理模型是一个常微分方程的规范形式模型捕捉系统的分岔结构。然后使用机器学习技术基于实验数据识别从该模型到实验观察的数据驱动映射。本文首先在一个货车der Pol振子和一个三自由度气动弹性模型上进行了数值模拟。然后,它被应用到模型的行为的物理气动弹性结构,在风洞试验中表现出极限环振荡。该方法被证明是一般的,数据高效,并提供良好的精度,没有任何先验知识的系统以外的分歧结构。
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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期刊: --
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