Using machine learning and crystal plasticity simulation to design damage 
resistant dual phase steels

Diehl, Martin; Kusampudi, Navyanth

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Using machine learning and crystal plasticity simulation to design damage resistant dual phase steels

Diehl, M., & Kusampudi, N. (2021). Using machine learning and crystal plasticity simulation to design damage resistant dual phase steels. Talk presented at Webinar: Metal Plasticity Seminar - Artificial Intelligence, Machine Learning and Big Data in Metal Plasticity. Leuven, Belgium. 2021-10-01.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0009-4C9B-4 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0009-5D23-8

Genre: Vortrag

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Diehl, Martin^{1, 2}, Autor
Kusampudi, Navyanth³, Autor

Affiliations:
1Department of Materials Engineering, KU Leuven, Kasteelpark Arenberg 44, Leuven 3001, Belgium; Department of Computer Science, KU Leuven, Celestijnenlaan 200 A, Leuven 3001, Belgium, ou_persistent22
2Integrated Computational Materials Engineering, Project Groups, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3069168
3Integrated Computational Materials Engineering, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3069168