Parameter identification of a fluid-structure system by deep-learning with an Eulerian formulation

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 Lam´e coefficients and the density of the solid. Hence an Eulerian monolithic solver is introduced. The numerical tests validate the deep-learning approach.