Inverse design of dual-phase steel microstructures using generative machine 
learning model and Bayesian optimization

Kusampudi, Navyanth; Diehl, Martin

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Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization

Kusampudi, N., & Diehl, M. (2022). Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization. Talk presented at Working Group Microstructural Mechanics, Deutsche Gesellschaft für Materialkunde e.V., Applications of Machine Learning for Mechanical Behavior of Materials. Online.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000B-7A4A-A Version Permalink: https://hdl.handle.net/21.11116/0000-000B-7A4B-9

Genre: Talk

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Creators:
Kusampudi, Navyanth¹, Author
Diehl, Martin², Author

Affiliations:
1Integrated Computational Materials Engineering, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3069168
2Department 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

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Language(s): eng - English

Dates: Created: 2022-11-16

Publication Status: Not specified

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Title: Working Group Microstructural Mechanics, Deutsche Gesellschaft für Materialkunde e.V., Applications of Machine Learning for Mechanical Behavior of Materials

Place of Event: Online

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Invited: Yes

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