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Updated: May 21, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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增强与地球移动器距离和组相似性的非线性动态的稀疏识别

Donglin Liu1, Alexandros Sopasakis1

  • 1Department of Mathematics, Lund University, 22362 Lund, Sweden.

Chaos (Woodbury, N.Y.)
|March 19, 2025
PubMed
概括

组相似性SINDy (GS-SINDy) 增强了从数据中发现非线性动态. 这种方法通过整合稀疏性和相似性措施来提高准确性和稳定性,特别是在杂或低数据条件下.

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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