Overview

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 propose an approach to discover a set of state variables that preserve the smoothness of the system dynamics and to construct a vector field representing the system’s dynamics equation, automatically from video streams without prior physical knowledge. The prominence and effectiveness of the proposed approach are demonstrated through both quantitative and qualitative analyses of various dynamical systems, including the prediction of characteristic frequencies and the identification of chaotic and limit cycle behaviors. This shows the potential of our approach to assist human scientists in scientific discovery.

Video (Click to YouTube)

Paper

Check out our paper linked here.

Codebase

Check out our codebase at https://github.com/generalroboticslab/automated_discovery_of_continuous_dynamics_from_videos

Citation

@misc{huang2024automateddiscoverycontinuousdynamics,
      title={Automated Discovery of Continuous Dynamics from Videos}, 
      author={Kuang Huang and Dong Heon Cho and Boyuan Chen},
      year={2024},
      eprint={2410.11894},
      archivePrefix={arXiv},
      primaryClass={eess.SY},
      url={https://arxiv.org/abs/2410.11894}, 
}     

Acknowledgment

This work is supported by ARL STRONG program under awards W911NF2320182 and W911NF2220113, by ARO W911NF2410405, by DARPA FoundSci program under award HR00112490372, and DARPA TIAMAT program under award HR00112490419.

Contact

If you have any questions, please feel free to contact Boyuan Chen. Kuang Huang and Dong Heon Cho contributed equally to this work.