國立虎尾科技大學 |

Accelerating Phase-Field Simulations of Materials Microstructures by Machine Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Accelerating Phase-Field Simulations of Materials Microstructures by Machine Learning./
作者:	Dai, Minyi.
面頁冊數:	1 online resource (150 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Materials science. -
電子資源:	click for full text (PQDT)
ISBN:	9798382720685

Accelerating Phase-Field Simulations of Materials Microstructures by Machine Learning.
Dai, Minyi.

Accelerating Phase-Field Simulations of Materials Microstructures by Machine Learning. - 1 online resource (150 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2024.

Includes bibliographical references

Phase-field modeling is a mesoscale computational method that can simulate the spatial and temporal evolution of microstructures. With no need to explicit track the interface between different phases, this method has been successfully applied to a variety of material system and physical phenomena. In this thesis, its application to magnetization switching and effective properties calculation is discussed first.On the other hand, the small grid size required to spatially resolve the narrow (down to 1-2 nanometers) diffuse interfaces in heterogeneous materials results in a large computational load, especially for large-scale microstructures. Moreover, for complex physical phenomena, it is challenging to accurately determine all the thermodynamic and kinetic parameters involved in a phase-field model.To address these issues, machine learning models to accelerate the prediction of microstructure properties are discussed. Specifically, simple machine learning regression models are employed to predict the phase diagram of magnetization switching behaviors in magnetic nanodisks. In addition, graph neural networks are used to predict various properties of polycrystalline materials, including magnetostriction, electrical conductivity and Young's modulus. These machine learning approaches have demonstrated high accuracy and computational efficiency. Furthermore, the interpretability and the transferability of these models are also discussed.The final chapter outlines future directions to integrate phase-field modeling with machine learning, aiming to further enhance the fast and accurate prediction of material properties and dynamics.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382720685Subjects--Topical Terms:

557839
Materials science.
Subjects--Index Terms:

Phase-field modelingIndex Terms--Genre/Form:

554714
Electronic books.

Accelerating Phase-Field Simulations of Materials Microstructures by Machine Learning.
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504 $a Includes bibliographical references
520 $a Phase-field modeling is a mesoscale computational method that can simulate the spatial and temporal evolution of microstructures. With no need to explicit track the interface between different phases, this method has been successfully applied to a variety of material system and physical phenomena. In this thesis, its application to magnetization switching and effective properties calculation is discussed first.On the other hand, the small grid size required to spatially resolve the narrow (down to 1-2 nanometers) diffuse interfaces in heterogeneous materials results in a large computational load, especially for large-scale microstructures. Moreover, for complex physical phenomena, it is challenging to accurately determine all the thermodynamic and kinetic parameters involved in a phase-field model.To address these issues, machine learning models to accelerate the prediction of microstructure properties are discussed. Specifically, simple machine learning regression models are employed to predict the phase diagram of magnetization switching behaviors in magnetic nanodisks. In addition, graph neural networks are used to predict various properties of polycrystalline materials, including magnetostriction, electrical conductivity and Young's modulus. These machine learning approaches have demonstrated high accuracy and computational efficiency. Furthermore, the interpretability and the transferability of these models are also discussed.The final chapter outlines future directions to integrate phase-field modeling with machine learning, aiming to further enhance the fast and accurate prediction of material properties and dynamics.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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