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NCTS Lecture in Applied Mathematics
 
15:00 - 16:00, November 13, 2018 (Tuesday)
Room 202, Astronomy-Mathematics Building, NTU
(台灣大學天文數學館 202室)
Parameter Identification of a Fluid-Structure System by Deep-Learning with an Eulerian Formulation
Olivier Pironneau (Université Paris VI & Academy des Sciences)

Abstract:

A simple fluid-structure problem is considered as a test to assess the feasibility of deep-learning algorithms for parameter identification. Tensorflow by Google is used and as it is a stochastic algorithm, provision must be made for the robustness of the large displacement fluid-structure simulator with respect to a wide range of values for the Lamé coefficients and the density of the solid. Hence an Eulerian monolithic solver is introduced.  The numerical tests validate the deep-learning approach.



 

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