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.
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De Lozzo, M., Klotz, P., Laurent, B. (2012). Learning of Spatio-temporal Dynamics in Thermal Engineering. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_22
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DOI: https://doi.org/10.1007/978-3-642-32909-8_22
Publisher Name: Springer, Berlin, Heidelberg
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