Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties-Reference-Cited by-同舟云学术

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Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties

Published:2024-06 Issue: Volume:16 Page:100544
ISSN:2666-8270
Container-title:Machine Learning with Applications
language:en
Short-container-title:Machine Learning with Applications

Author:

Nguyen Binh Duong,Potapenko Pavlo,Demirci Aytekin,Govind Kishan,Bompas Sébastien,Sandfeld Stefan^ORCID

Funder

Forschungszentrum Jülich GmbH

European Research Council

Publisher

Elsevier BV

Reference57 articles.

1. Interstitial strengthening in fcc metals and alloys;Baker;Advanced Powder Materials,2022

2. Algorithms for hyper-parameter optimization;Bergstra,2011

3. Comparing different deep learning architectures for classification of chest radiographs;Bressem;Scientific Reports,2020

4. On spinodal decomposition;Cahn;Acta Metallurgica,1961

5. Materials science and engineering an introduction;Callister,2007

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