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- Sparse identification of non-linear dynamics - Wikipedia
Sparse identification of non-linear dynamics Sparse identification of nonlinear dynamics (SINDy) is a data-driven algorithm for obtaining dynamical systems from data [1]
- SINDYy方法的原理与实践-从数据中识别动力系统 - 知乎
三、 总结： 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。 该方法通过构建包含各种可能非线性项的函数库，并使用稀疏优化技术来确定哪些项在描述系统行为时是最重要的。
- SINDy-RL for interpretable and efficient model-based . . . - Nature
Here, authors introduce SINDy-RL, a framework that combines sparse system identification with reinforcement learning to yield efficient, interpretable, and high-performing control policies
- SINDy-KANs: Sparse identification of non-linear dynamics through . . .
Kolmogorov-Arnold networks (KANs) have arisen as a potential way to enhance the interpretability of machine learning However, solutions learned by KANs are not necessarily interpretable, in the sense of being sparse or parsimonious Sparse identification of nonlinear dynamics (SINDy) is a complementary approach that allows for learning sparse equations for dynamical systems from data; however
- GitHub - dynamicslab pysindy: A package for the sparse identification . . .
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 (2016a)
- SINDy: 从数据中反推系统的动力学 | Bohrium
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。
- An introduction to Sparse Identification of Nonlinear Dynamical systems . . .
The SINDy object encapsulates one class object from each of these three submodules and uses them, along with a user-supplied data matrix, to find a governing dynamical system The beginning tutorial walks through an example showing how this works using a toy dataset
- SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study
While SINDy is often portrayed as an appealing strategy for pursuing physics-based learning, our analysis highlights two weaknesses, i e , the difficulties in applying this technique when dealing with unobserved states and non-smooth dynamics
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