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Cours Année : 2019

Function estimation in inverse heat transfer problems

Résumé

This lecture presents some commonly-used numerical algorithms devoted to optimization, that is maximizing or, more often minimizing a given function of several variables. The goal is function estimation. At rst, some general mathematical tools are presented. Some gradient-free optimization algorithms are presented and then some gradient-type methods are pointed out with pros and cons for each method. The gradient of the function to be minimized is presented according to three distinct methods: nite di erence, forward di erentiation and the use of the additional adjoint-state problem. The last part presents some practical studies where some tricks are given, along with some numerical results.
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Dates et versions

hal-02894729 , version 1 (05-12-2022)

Identifiants

  • HAL Id : hal-02894729 , version 1

Citer

Yann Favennec. Function estimation in inverse heat transfer problems. Doctoral. CNRS : METTI 7. Porquerolles, France. 2019, pp.39. ⟨hal-02894729⟩
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