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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.11894 |
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| _version_ | 1866916704447102976 |
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| author | Huang, Kuang Cho, Dong Heon Chen, Boyuan |
| author_facet | Huang, Kuang Cho, Dong Heon Chen, Boyuan |
| contents | Dynamical systems form the foundation of scientific discovery, traditionally modeled with predefined state variables such as the angle and angular velocity, and differential equations such as the equation of motion for a single pendulum. We introduce a framework that automatically discovers a low-dimensional and operable representation of system dynamics, including a set of compact state variables that preserve the smoothness of the system dynamics and a differentiable vector field, directly from video without requiring prior domain-specific knowledge. The prominence and effectiveness of the proposed approach are demonstrated through both quantitative and qualitative analyses of a range of dynamical systems, including the identification of stable equilibria, the prediction of natural frequencies, and the detection of of chaotic and limit cycle behaviors. The results highlight the potential of our data-driven approach to advance automated scientific discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11894 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Discovery of Operable Dynamics from Videos Huang, Kuang Cho, Dong Heon Chen, Boyuan Systems and Control Machine Learning Image and Video Processing Chaotic Dynamics Dynamical systems form the foundation of scientific discovery, traditionally modeled with predefined state variables such as the angle and angular velocity, and differential equations such as the equation of motion for a single pendulum. We introduce a framework that automatically discovers a low-dimensional and operable representation of system dynamics, including a set of compact state variables that preserve the smoothness of the system dynamics and a differentiable vector field, directly from video without requiring prior domain-specific knowledge. The prominence and effectiveness of the proposed approach are demonstrated through both quantitative and qualitative analyses of a range of dynamical systems, including the identification of stable equilibria, the prediction of natural frequencies, and the detection of of chaotic and limit cycle behaviors. The results highlight the potential of our data-driven approach to advance automated scientific discovery. |
| title | Automated Discovery of Operable Dynamics from Videos |
| topic | Systems and Control Machine Learning Image and Video Processing Chaotic Dynamics |
| url | https://arxiv.org/abs/2410.11894 |