Abstract
Abstract 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.
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