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Article

Deep Learning Sequence Methods in Multiphysics Modeling of Steel Solidification

by 1,2,* and 1,2
1
National Center for Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
2
Department of Mechanical Science and Engineering, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Roberto Montanari
Metals 2021, 11(3), 494; https://doi.org/10.3390/met11030494
Received: 15 February 2021 / Revised: 10 March 2021 / Accepted: 12 March 2021 / Published: 17 March 2021
(This article belongs to the Special Issue Material Modeling in Multiphysics Simulation)
The solidifying steel follows highly nonlinear thermo-mechanical behavior depending on the loading history, temperature, and metallurgical phase fraction calculations (liquid, ferrite, and austenite). Numerical modeling with a computationally challenging multiphysics approach is used on high-performance computing to generate sufficient training and testing data for subsequent deep learning. We have demonstrated how the innovative sequence deep learning methods can learn from multiphysics modeling data of a solidifying slice traveling in a continuous caster and correctly and instantly capture the complex history and temperature-dependent phenomenon in test data samples never seen by the deep learning networks. View Full-Text
Keywords: sequence deep learning; neural networks; casting; steel; solidification; multiphysics sequence deep learning; neural networks; casting; steel; solidification; multiphysics
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MDPI and ACS Style

Koric, S.; Abueidda, D.W. Deep Learning Sequence Methods in Multiphysics Modeling of Steel Solidification. Metals 2021, 11, 494. https://doi.org/10.3390/met11030494

AMA Style

Koric S, Abueidda DW. Deep Learning Sequence Methods in Multiphysics Modeling of Steel Solidification. Metals. 2021; 11(3):494. https://doi.org/10.3390/met11030494

Chicago/Turabian Style

Koric, Seid, and Diab W. Abueidda 2021. "Deep Learning Sequence Methods in Multiphysics Modeling of Steel Solidification" Metals 11, no. 3: 494. https://doi.org/10.3390/met11030494

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