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

Citations