Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Automated reverse engineering of nonlinear dynamical systems.

Josh Bongard1, Hod Lipson

  • 1Mechanical and Aerospace Engineering and Computing and Information Science, Cornell University, Ithaca, NY 14853, USA. josh.bongard@uvm.edu

Proceedings of the National Academy of Sciences of the United States of America
|June 8, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Learning realistic lip motions for humanoid face robots.

Science robotics·2026
Same author

The topology of synergy: Linking topological and information-theoretic approaches to higher-order interactions in complex systems.

PLoS computational biology·2025
Same author

Identification of brain-like complex information architectures in embryonic tissue of <i>Xenopus laevis</i> organoids.

Communicative & integrative biology·2025
Same author

Robot metabolism: Toward machines that can grow by consuming other machines.

Science advances·2025
Same author

Evolution of adaptive force chains in reconfigurable granular metamaterials.

Soft matter·2025
Same author

Author Correction: Ab initio structure solutions from nanocrystalline powder diffraction data via diffusion models.

Nature materials·2025
Same journal

Costunolide ameliorates autoimmune uveitis by targeting USP15 to suppress TNF-α-induced retinal endothelial inflammation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A ligandable PNT domain establishes ERG as a directly targetable oncogenic driver in prostate cancer.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Identification of cellular intermediates unveils unique enzymes for flagellar glycan biosynthesis in <i>Clostridioides difficile</i>.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

The structure of correlated variability reflects task-relevant information in sensory neurons.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Shared neurogenetic substrates of nonplanning impulsivity and procrastination.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

HIV-1 capsid interactions with Nuclear Pore Complex components support nuclear entry via affinity gradient.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

This study introduces a novel method for automatically generating symbolic equations of nonlinear coupled dynamical systems from time series data. This approach enables symbolic modeling for complex systems, advancing scientific understanding and reverse engineering capabilities.

Area of Science:

  • Complex Systems Science
  • Nonlinear Dynamics
  • Computational Science

Background:

  • Understanding complex nonlinear dynamics is crucial across science and engineering.
  • Symbolically modeling networked systems from observational data remains a significant challenge.
  • Existing automated methods often yield linear models or require manual nonlinear model specification.

Purpose of the Study:

  • To develop a method for automatically generating symbolic equations of nonlinear coupled dynamical systems directly from time series data.
  • To enable symbolic modeling for systems described by ordinary nonlinear differential equations with observable time series.
  • To overcome limitations of previous automated symbolic modeling approaches.

Main Methods:

  • Modeling each variable of the coupled system separately.

Related Experiment Videos

  • Intelligently perturbing and destabilizing the system to reveal less observable characteristics.
  • Automatic simplification of generated equations during the modeling process.

    • Main Results:

    • Successful generation of symbolic equations for nonlinear coupled dynamical systems from time series data.
    • Demonstration of the method's applicability on simulated and real-world systems in mechanics, ecology, and systems biology.
    • The method successfully models nonlinear coupled systems, a significant improvement over prior automated techniques.

    Conclusions:

    • Automated symbolic modeling of nonlinear dynamical systems from data is now feasible.
    • Symbolic models offer explanatory value, unlike purely numerical models.
    • Model-free symbolic nonlinear system identification will increasingly aid the understanding of complex systems.