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

Genre: Talk

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Creators:
Diehl, Martin^{1, 2}, Author
Kusampudi, Navyanth³, Author

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

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Language(s): eng - English

Dates: Created: 2021-10-01

Publication Status: Not specified

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Title: Webinar: Metal Plasticity Seminar - Artificial Intelligence, Machine Learning and Big Data in Metal Plasticity

Place of Event: Leuven, Belgium

Start-/End Date: 2021-10-01

Invited: Yes

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