Finding Alternatives and Reduced Formulations for Process-Based Models

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@Article{Bernhardt:2008:EC,
  author =       "Knut Bernhardt",
  title =        "Finding Alternatives and Reduced Formulations for
                 Process-Based Models",
  journal =      "Evolutionary Computation",
  year =         "2008",
  volume =       "16",
  number =       "1",
  pages =        "63--88",
  month =        "Spring",
  keywords =     "genetic algorithms, genetic programming, Model
                 reduction, complexity, dimension reduction",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/evco.2008.16.1.63",
  size =         "26 pages",
  abstract =     "This paper addresses the problem of model complexity
                 commonly arising in constructing and using
                 process-based models with intricate interactions. Apart
                 from complex process details the dynamic behaviour of
                 such systems is often limited to a discrete number of
                 typical states. Thus, models reproducing the system's
                 processes in all details are often too complex and
                 over-parameterised. In order to reduce simulation times
                 and to get a better impression of the important
                 mechanisms, simplified formulations are desirable.

                 In this work a data adaptive model reduction scheme
                 that automatically builds simple models from complex
                 ones is proposed. The method can be applied to the
                 transformation and reduction of systems of ordinary
                 differential equations. It consists of a multistep
                 approach using a low dimensional projection of the
                 model data followed by a Genetic Programming/Genetic
                 Algorithm hybrid to evolve new model systems. As the
                 resulting models again consist of differential
                 equations, their process-based interpretation in terms
                 of new state variables becomes
                 possible.

                 Transformations of two simple models with oscillatory
                 dynamics, simulating a mathematical pendulum and
                 predator-prey interactions respectively, serve as
                 introductory examples of the method's application. The
                 resulting equations of force indicate the predator-prey
                 system's equivalence to a nonlinear oscillator. In
                 contrast to the simple pendulum it contains driving and
                 damping forces that produce a stable limit cycle.",
  notes =        "CVODE, SUNDIALS",
}

Genetic Programming entries for Knut Bernhardt

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