[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127657-en":3,"doc-seo-127657-105":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":4,"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},127657,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Turbulence Modeling in Core-Collapse Supernovae with Machine Learning - Dissertation","Turbulence regulation in core-collapse supernovae is modeled using machine learning to develop and validate turbulence-closure components for both 3D MHD turbulence and 1D CCSN evolution. The work introduces the Sapsan framework, defines supervised and unsupervised learning pathways, and includes physics-informed learning for subgrid stress modeling. Results assess performance for stationary and dynamic turbulence, then transfer learned subgrid prescriptions into 1D CCSN simulations to quantify effects on convection/turbulence and compare against baseline models.","UC Santa Cruz  \nUC Santa Cruz Electronic Theses and Dissertations  \nTitle  \nTurbulence Modeling in Core-Collapse Supernovae with Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/4ks4m0kk](https://escholarship.org/uc/item/4ks4m0kk)  \nAuthor  \nKarpov, Platon  \nPublication Date  \n2023  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, availalbe at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \n[Peer reviewed|Thesis/dissertation](Peer reviewed|Thesis/dissertation)  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nSANTA CRUZ  \nTURBULENCE MODELING IN CORE-COLLAPSE SUPERNOVAE WITH  \nMACHINE LEARNING  \nA dissertation submitted in partial satisfaction of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nin  \nASTRONOMY AND ASTROPHYSICS  \nwith an emphasis in SCIENTIFIC COMPUTING  \nby  \nPlaton Igorevich Karpov  \nSeptember 2023  \nThe Dissertation of Platon Igorevich Karpov is approved:  \n\n| Professor Stan Woosley, Chair |\n| --- |\n| Professor Constance M. Rockosi |\n| Professor J. Xavier Prochaska |\n| Chris L. Fryer, Ph.D. |\n\nChengkun Huang, Ph.D.  \nPeter Biehl  \nVice Provost and Dean of Graduate Studies  \nTable of Contents  \nList of Figures v  \nList of Tables vii  \nAbstract viii  \nDedication x  \nAcknowledgments xi  \n1 Introduction 1  \n1.1 Core-Collapse Supernova Mechanism .................. 3  \n1.2 Turbulence ................................ 6  \n1.3 Machine Learning for Turbulence Closure Models ............ 11  \n1.3.1 Supervised Machine Learning .................. 12  \n1.3.2 Unsupervised Machine Learning ................. 14  \n1.3.3 Physics Informed Machine Learning ............... 15  \n1.3.4 Machine Learning in Astrophysics ................ 17  \n2 Sapsan Framework 18  \n2.1 Statement of Need ............................. 19  \n2.2 Framework ................................ 22  \n2.3 Applications ................................ 26  \n2.3.1 Hydro simulations ........................ 26  \n2.3.2 Supernovae ............................ 27  \n3 Modeling 3D MHD Turbulence 31  \n3.1 Introduction ................................ 32  \n3.2 Formalism ................................. 40  \n3.2.1 Filtering .............................. 40  \n3.2.2 MHD equations-Unfiltered ................... 41  \n3.2.3 MHD equations-Filtered .................... 42  \n3.3 Subgrid Modeling ............................. 43  \n3.3.1 Gradient model .......................... 43  \n3.3.2 Machine Learning Pipeline .................... 44  \n3.3.3 Machine Learning Models .................... 46  \n3.3.4 Datasets .............................. 50  \n3.4 Results & Discussion ........................... 54  \n3.4.1 Stationary Turbulence ...................... 54  \n3.4.2 Dynamic Turbulence ....................... 56  \n3.5 Conclusion ................................ 63  \n4 Machine Learning for Core-Collapse Supernovae: 1D Models 71  \n4.1 Introduction ................................ 73  \n4.2 Formalism ................................. 78  \n4.2.1 Convection or Turbulence? .................... 78  \n4.2.2 1D Code ............................. 79  \n4.2.3 Turbulence Extraction ...................... 83  \n4.3 Turbulence Model ............................. 84  \n4.3.1 Machine Learning Model ..................... 84  \n4.3.2 Basis 3D Dataset ......................... 86  \n4.4 Results ................................... 92  \n4.4.1 ML Training ........................... 92  \n4.4.2 Baseline 1D CCSN ........................ 93  \n4.4.3 1D CCSN with ML subgrid model ................ 96  \n4.5 Discussion ................................. 97  \n4.5.1 Effect of turbulence in 1D models ................ 100  \n4.5.2 Comparison & Limitations .................... 103  \n4.6 Conclusion & Future Directions ..................... 108  \n5 Conclusion 125  \n5.1 Paper Summary .............................. ","cbCaiqWnzSqa19DC","https://ap.wps.com/l/cbCaiqWnzSqa19DC","pdf",2980192,1,159,"English","en",105,"# Introduction\n## Core-Collapse Supernova Mechanism\n## Turbulence\n## Machine Learning for Turbulence Closure Models\n# Sapsan Framework\n## Statement of Need\n## Framework\n## Applications\n# Modeling 3D MHD Turbulence\n## Formalism\n## Subgrid Modeling\n## Results & Discussion\n# Machine Learning for Core-Collapse Supernovae: 1D Models\n## Formalism\n## Turbulence Model\n## Results\n## Discussion\n# Conclusion","[{\"question\":\"What problem does this dissertation address?\",\"answer\":\"It addresses how to model turbulence and turbulence closure in core-collapse supernovae using machine learning, with emphasis on linking learned subgrid effects to 1D supernova evolution.\"},{\"question\":\"What is the Sapsan framework used for?\",\"answer\":\"Sapsan provides a structured workflow for applying machine learning to turbulence-closure models, including problem framing, modeling components, and application to supernova-related cases.\"},{\"question\":\"How are machine learning methods organized in the work?\",\"answer\":\"The dissertation distinguishes supervised learning, unsupervised learning, and physics-informed machine learning, and then applies these ideas to turbulence and subgrid modeling pipelines.\"}]","Turbulence Modeling in Core-Collapse Supernovae with Machine Learning - 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