Testing the Generalizability of Deep Learning Based Plausibility Detection with Unknown Finite Element Simulations

Bickel S, Götz S, Wartzack S (2024)

Publication Type: Conference contribution

Publication year: 2024

Series: NordDESIGN

Pages Range: 51-60

Conference Proceedings Title: DS 130: Proceedings of , Reykjavik, Iceland, 12th - 14th August 2024

Event location: Reykjavik IS

DOI: 10.35199/NORDDESIGN2024.6

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.

Authors with CRIS profile

Sebastian Bickel Chair of Engineering Design (KTmfk) Stefan Götz Chair of Engineering Design (KTmfk) Sandro Wartzack Chair of Engineering Design (KTmfk)

How to cite

APA:

Bickel, S., Götz, S., & Wartzack, S. (2024). Testing the Generalizability of Deep Learning Based Plausibility Detection with Unknown Finite Element Simulations. In DS 130: Proceedings of , Reykjavik, Iceland, 12th - 14th August 2024 (pp. 51-60). Reykjavik, IS.

MLA:

Bickel, Sebastian, Stefan Götz, and Sandro Wartzack. "Testing the Generalizability of Deep Learning Based Plausibility Detection with Unknown Finite Element Simulations." Proceedings of the NordDesign 2024, Reykjavik 2024. 51-60.

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