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SINDy（Sparse Identification of Nonlinear Dynamics）方法是一种强大的 数据驱动 技术，用于从时间序列数据中推导出系统的非线性动力学方程。 SINDy（稀疏识别动力学）算法是一种新兴的数学建模方法，它通过数学模型来预测复杂系统的动态行为。 这种方法在处理非线性系统和高维数据时表现出色，尤其在物理学、化学和生物学等领域有着广泛的应用前景。 Pysindy is a package for system identification, primarily revolving around the method of sparse identification of nonlinear dynamical systems (sindy) method introduced in brunton et al
近年来，一种名为SINDy（Sparse Identification of Nonlinear Dynamics，非线性动力学的稀疏识别）的系统辨识方法受到了广泛关注，它能够从数据中自动发现动态系统的数学模型。 y = 0.5 * np.exp(t) X = np.stack((x,y),axis = - 1) #使用默认方法、特征库和优化器实例化对象，然后进行数据拟合 model = ps.SINDy( feature_names = ["x", "y"] ) model.fit(X,t = t) 这篇notebook介绍一种简单而有效的从动力学数据中反推系统满足的动力学方程的方法Sparse Identification of Nonlinear Dynamics (SINDy)，配合降噪以及降维的手段，该方法可以准确地还原出包括流体力学系统在内的复杂动力学系统的动力学方程。
Sindy is a model discovery method which uses sparse regression to infer nonlinear dynamical systems from measurement data
The resulting models are inherently interpretable and generalizable. 非线性动力学的稀疏识别 (SINDy)简介 标签： 数模竞赛 社区小助手 2024-08-16 17:06:51
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