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Learning of Spatio-temporal Dynamics in Thermal Engineering

  • Matthias De Lozzo
  • Patricia Klotz
  • Béatrice Laurent
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)

Abstract

Thermal engineering deals with the estimation of the temperature at different points and instants for a given set of boundary and initial conditions. For this, an analytic model replaces accurate but time-expensive numerical simulation models; it is independent of the boundary conditions and parameterized by the statistical learning of multidimensional temporal trajectories. This black-box model is a recursive neural network emulating the temperatures of interest over time from the only knowledge of initial conditions and exogenous variables.

The number of hidden neurons is selected by a non-asymptotic approach based upon the minimization of a penalyzed criterion. Methods like the slope heuristic and the dimension jump enable the calibration of the penalty constant in presence of a n-sample. In practice, their extrapolation to dependent data gives accurate results in the sense of the mean square error.

The surrogate model and the model selection are successfully applied to an industrial benchmark.

Keywords

recursive neural network nonuniform time step model selection penalized criterion thermal engineering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Matthias De Lozzo
    • 1
    • 2
  • Patricia Klotz
    • 2
  • Béatrice Laurent
    • 3
  1. 1.EPSILON IngénierieLabège CedexFrance
  2. 2.The French Aerospace LabONERAToulouse Cedex 4France
  3. 3.Institut de Mathématiques de ToulouseINSA ToulouseToulouse Cedex 4France

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