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Application of Artificial Neural Network accelerating a porous media FE 2 homogenization scheme
Author(s) -
Bartel Florian,
Ricken Tim,
Schröder Jörg,
Bluhm Joachim
Publication year - 2019
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201900381
Subject(s) - homogenization (climate) , artificial neural network , computer science , nonlinear system , porous medium , artificial intelligence , computer engineering , algorithm , porosity , materials science , biodiversity , ecology , physics , quantum mechanics , composite material , biology
Multiscale techniques, which include information of discrete lower level substructures of real material, are state of the art methods of current researches. This technology has the advantage of achieving more accurate results, by imaging the real geometry information from the microscopic level. In addition, it provides the opportunity to design a certain microstructure which fulfills the specific requirements at a macroscopic level. The drawback lies on the increasing computational effort. Simulation of a 3‐dimensional, nonlinear, time‐dependent, coupled, two‐scale problem with industrial relevance, could cause unacceptable runtimes. There are several strategies to overcome this disadvantage, such as parallelization, analytical derivatives and various surrogate models. This contribution shows the feasibility of storing microstructural information in an Artificial Neural Network, in order to reduce computational runtime.