Im Rahmen des Forschungskolloquiums für Abschlussarbeitende, Doktoranden und Habilitanden laden wir herzlich ein zum Vortrag

Efficient Multiscale Modeling of Heterogeneous Materials Using Deep Neural Networks,

Professor Dr.-Ing. Fadi Aldakheel, Gottfried Wilhelm Leibniz Universität Hannover, Institut für Baumechanik und Numerische Mechanik (IBNM)

Material modeling using modern numerical methods accelerates the design process and reduces the costs of developing new products. However, for multiscale modeling of heterogeneous materials, the well-established homo-genization techniques remain computationally expensive for high accuracy levels. In this contribution, a machine learning approach, convolutional neural networks (CNNs), is proposed as a computationally efficient solution method that is capable of providing a high level of accuracy. In this work, the data-set used for the training process, as well as the numerical tests, consists of artificial/real microstructural images (“input”). Whereas, the output is the homogenized stress of a given representative volume element. The model performance is demonstrated by means of examples and compared with  traditional homogenization methods. As the examples illustrate, high accuracy in predicting the homogenized stresses, along with a significant reduction in the computation time, were achieved using the developed CNN model.
 

Das Kolloquium findet am 04.07.23 um 14:00 Uhr im Campus Center, Hörsaal 4 statt. Wir freuen uns auf Ihr Kommen.