[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124858-en":3,"doc-seo-124858-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124858,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","A Machine Learning Assisted Multi-scale study of damage evolution under mechanical deformation in nanostructured materials - Dissertation","A Machine Learning Assisted Multi-scale study of damage evolution under mechanical deformation in nanostructured materials investigates how hierarchical and concurrent multi-scale modeling can be used to understand deformation-driven damage. The research builds atomistic descriptions of nanostructured systems, then develops machine-learning assisted surrogate models to bridge length and time scales. Physics-informed learning, feature selection, and optimized atomistic data generation support scale transitions and improve prediction of damage behavior under mechanical loading, targeting more reliable modeling within an ICME context.","UC San Diego  \nUC San Diego Electronic Theses and Dissertations  \nTitle  \nA Machine Learning Assisted Multi-scale study of damage evolution under mechanical deformation in nanostructured materials  \nPermalink  \n[https://escholarship.org/uc/item/56q8299v](https://escholarship.org/uc/item/56q8299v)  \nAuthor  \nHasan, Md Shahrier  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA SAN DIEGO  \nSAN DIEGO STATE UNIVERSITY  \nA Machine Learning Assisted Multi-scale study of damage evolution under mechanical  \ndeformation in nanostructured materials  \nA Dissertation submitted in partial satisfaction of the requirements  \nfor the degree Doctor of Philosophy  \nin  \nEngineering Science (Mechanical and Aerospace Engineering)  \nby  \nMd Shahrier Hasan  \nCommittee in charge:  \nUniversity of California San Diego  \nProfessor Shengqiang, Cai, Co-Chair  \nProfessor Javier Garay  \nProfessor Shyue Ping Ong  \nProfessor Shabnam Semnani  \nSan Diego State University  \nProfessor Wenwu Xu, Co-Chair  \nProfessor Juanfei Xie  \n©  \nMd Shahrier Hasan, 2024  \nAll rights reserved  \nThe Dissertation of Md Shahrier Hasan is approved, and it is acceptable in quality and form for publication on microfilm and electronically.  \nCo-chair  \nCo-chair  \nUniversity of California San Diego San Diego State University  \n2024  \nTABLE OF CONTENTS  \nDISSERTATION APPROVAL PAGE ...........................................................................................iii  \nTABLE OF CONTENTS................................................................................................................. iv  \nLIST OF FIGURES .......................................................................................................................viii  \nLIST OF TABLES .........................................................................................................................xiii  \nACKNOWLEDGEMENTS ........................................................................................................... xiv  \nVITA ............................................................................................................................................... xv  \nABSTRACT OF THE DISSERTATION ..................................................................................... xvii  \nChapter 1 Introduction ...................................................................................................................... 1  \n1.1 Multi-scale Modeling of Materials ............................................................................................. 1  \n1.1.1 Concurrent Multi-scale Modeling............................................................................................ 4  \n1.1.2 Hierarchical Multi-scale Modeling .......................................................................................... 6  \n1.2 Hierarchical Multiscale Modeling in the context of ICME ........................................................ 8  \n1.3 Motivation ................................................................................................................................. 11  \n1.4 Objectives (slightly more comprehensive) ............................................................................... 14  \nChapter 2 Atomistic Modeling of Nanostructured Materials ......................................................... 15  \n2.1 Atomistic Modeling Principle ................................................................................................... 15  \n2.2 Single and multiphase Nano-structured Materials .................................................................... 17  \n2.2.1 Nanocrystalline Metals .......................................................................................................... 18  \n2.2.2 Metal Matrix Nanocomposites ..........................................................................","cbCaibpU6Y6eb0no","https://ap.wps.com/l/cbCaibpU6Y6eb0no","pdf",51404215,1,125,"English","en",105,"# Chapter 1 Introduction\n## 1.1 Multi-scale Modeling of Materials\n## 1.2 Hierarchical Multiscale Modeling in the context of ICME\n## 1.3 Motivation\n## 1.4 Objectives (slightly more comprehensive)\n# Chapter 2 Atomistic Modeling of Nanostructured Materials\n## 2.1 Atomistic Modeling Principle\n## 2.2 Single and multiphase Nano-structured Materials\n## 2.3 Atomistic Modeling of Nano-crystalline Magnesium\n## 2.4 Atomistic Modeling in Metal Matrix Nanocomposites\n# Chapter 3 Machine Learning Assisted Surrogate Modeling\n## 3.1 Machine Learning in Material Research\n## 3.2 Physics Informed Machine Learning\n## 3.3 Machine Learning for Scale Bridging\n## 3.4 Atomistic data generation and Optimization\n## 3.5 Feature Selection\n## 3.6 Database Optimization","[{\"question\":\"What modeling approaches are presented in the introduction chapter?\",\"answer\":\"The introduction covers concurrent multi-scale modeling and hierarchical multi-scale modeling, including hierarchical multiscale modeling within an ICME context.\"},{\"question\":\"Which nanostructured material systems are addressed in the atomistic modeling chapter?\",\"answer\":\"The chapter discusses single and multiphase nano-structured materials, including nanocrystalline metals, metal matrix nanocomposites, and atomistic modeling of nano-crystalline magnesium.\"},{\"question\":\"How does machine learning support the surrogate modeling in this dissertation?\",\"answer\":\"Machine learning is used for surrogate modeling and scale bridging, with physics-informed methods, optimized atomistic data generation, and feature selection for damage classification and stress regression.\"}]","A Machine Learning Assisted Multi-scale study of damage evolution under mechanical deformation in nanostructured materials - Dissertation | 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