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Testing the Generalizability of Deep Learning Based Plausibility Detection with Unknown Finite Element Simulations
Bickel, Sebastian; Stefan, Goetz; Sandro, Wartzack
Type:
Year:
2024
Editor:
Malmqvist, J.; Candi, M.; Saemundsson, R. J.; Bystrom, F. and Isaksson, O.
Author:
Series:
NordDESIGN
Institution:
Friedrich-Alexander-Universitat Erlangen-Nurnberg, Engineering Design, Germany
Page(s):
51-60
DOI number:
ISBN:
978-1-912254-21-7
Abstract:
Shorter development times and greater product variety are necessary in the current business world. These challenges can be managed with virtual testing methods, but these require experienced engineers. To counteract the shortage, a data-driven plausibility detection was developed. The plausibility is determined with Deep Learning models that are trained on existing simulations. The practical application requires the models to work with simulations not included in the training data. Therefore, this paper analyzes the transferability of a plausibility detection model to new, unknown instances.
Keywords:
