Automated reverse engineering of nonlinear dynamical systems

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@Article{Bongard:2007:PNAS,
  author =       "Josh Bongard and Hod Lipson",
  title =        "Automated reverse engineering of nonlinear dynamical
                 systems",
  journal =      "PNAS, Proceedings of the National Academy of Sciences
                 of the United States of America",
  year =         "2007",
  volume =       "104",
  number =       "24",
  pages =        "9943--9948",
  month =        "12 " # jun,
  keywords =     "genetic algorithms, genetic programming, Physical
                 Sciences, Computer Sciences, coevolution, modelling,
                 symbolic identification",
  doi =          "doi:10.1073/pnas.0609476104",
  size =         "6 pages",
  abstract =     "Complex nonlinear dynamics arise in many fields of
                 science and engineering, but uncovering the underlying
                 differential equations directly from observations poses
                 a challenging task. The ability to symbolically model
                 complex networked systems is key to understanding them,
                 an open problem in many disciplines. Here we introduce
                 for the first time a method that can automatically
                 generate symbolic equations for a nonlinear coupled
                 dynamical system directly from time series data. This
                 method is applicable to any system that can be
                 described using sets of ordinary nonlinear differential
                 equations, and assumes that the (possibly noisy) time
                 series of all variables are observable. Previous
                 automated symbolic modeling approaches of coupled
                 physical systems produced linear models or required a
                 nonlinear model to be provided manually. The advance
                 presented here is made possible by allowing the method
                 to model each (possibly coupled) variable separately,
                 intelligently perturbing and destabilising the system
                 to extract its less observable characteristics, and
                 automatically simplifying the equations during
                 modelling. We demonstrate this method on four simulated
                 and two real systems spanning mechanics, ecology, and
                 systems biology. Unlike numerical models, symbolic
                 models have explanatory value, suggesting that
                 automated reverse engineering approaches for model-free
                 symbolic nonlinear system identification may play an
                 increasing role in our ability to understand
                 progressively more complex systems in the future.",
  notes =        "Cited by Philosophy of Science Machine Science James
                 A. Evans and Andrey Rzhetsky Science 23 July 2010: Vol.
                 329 no. 5990 pp. 399-400 DOI:10.1126/science.1189416",
}

Genetic Programming entries for Josh C Bongard Hod Lipson