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Thermophysical characterization of materials at high temperatures by solving inverse problems within the bayesian framework of statistics
Philippe Le Masson and Helcio R. B. Orlande

Inverse heat transfer problems deal with the estimation of parameters or functions appearing in the mathematical formulation of problems in thermal sciences, by utilizing measurements of dependent variables of the formulation. Inverse problems are extremely useful for the indirect measurement of thermophysical properties, in particular for challenging situations involving high temperatures, where coupled multi-physics phenomena and nonlinearities must be taken into account. In this paper, basic inverse problem concepts are reviewed. Solution techniques within the Bayesian framework of statistics are briefly described and applied to two inverse problems related to the authors´ experience on the estimation of thermophysical properties at high temperatures.

Keywords: Thermophysical properties, inverse problems, Bayesian framework, Maximum a Posteriori objective function, Markov chain Monte Carlo method

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