Toward efficient parameter sweeping and optimisation of fluid performance models by Taylor-model based symbolic numerical integration

نویسندگان

  • R. A. Hayden
  • Richard A. Hayden
چکیده

Fluid-approximation or mean-field techniques are currently very popular approaches to the efficient analysis of massivelyparallel Markov models. In this paper, we exploit the ODE-representation of these approximations to develop efficient techniques for simultaneous partially-symbolic numerical integration over parameter ranges. In particular, we borrow the Taylor model data structure of Makino and Berz [14] from the field of verified numerical integration. We use this to compute, in an efficient manner, tight bounds on the range of the ODE solutions over time with parameters lying in large intervals. This has applications to fast parameter sweeping, sensitivity analysis and global optimisation of model parameters in perfomance models amenable to fluid approximation.

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تاریخ انتشار 2011