東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Accelerating Phase-Field Simulations...

Dai, Minyi.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Accelerating Phase-Field Simulations of Materials Microstructures by Machine Learning./
Author:	Dai, Minyi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	150 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Materials science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31299760
ISBN:	9798382720685

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

Accelerating Phase-Field Simulations of Materials Microstructures by Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 150 p.

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

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

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.

ISBN: 9798382720685Subjects--Topical Terms:

543314
Materials science.
Subjects--Index Terms:

Phase-field modeling

Accelerating Phase-Field Simulations of Materials Microstructures by Machine Learning.
LDR:02814nmm a2200385 4500 001 2403672
005 20241118135910.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798382720685
035 $a (MiAaPQ)AAI31299760
035 $a AAI31299760
040 $a MiAaPQ $c MiAaPQ
100 1 $a Dai, Minyi. $3 3773941
245 1 0 $a Accelerating Phase-Field Simulations of Materials Microstructures by Machine Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 150 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisor: Hu, Jiamian.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2024.
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.
590 $a School code: 0262.
650 4 $a Materials science. $3 543314
650 4 $a Electromagnetics. $3 3173223
650 4 $a Nanoscience. $3 587832
653 $a Phase-field modeling
653 $a Microstructures
653 $a Nanodisks
653 $a Machine learning
653 $a Nanometers
690 $a 0794
690 $a 0565
690 $a 0607
710 2 $a The University of Wisconsin - Madison. $b Materials Science and Engineering. $3 3695135
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 0262
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31299760