[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119134-en":3,"doc-seo-119134-105":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},119134,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Physics-Informed Machine Learning for the Earth Sciences - Applications to Glaciology and Paleomagnetism - Doctoral Dissertation","This dissertation studies the application of machine learning in Glaciology and Paleomagnetism, emphasizing recent advances that inject physical constraints as inductive biases into data-driven statistical and ML methods. The work introduces physics-informed machine learning in Chapter 1 and develops neural differential equation approaches for ice-flow modelling. Differentiable programming from Chapter 2 enables inversion and calibration of internal ice viscosity for mountain glaciers under different climates, leading to ODINN . jl, a multilanguage Julia-Python package for glacier–climate interaction modelling. Chapter 5 quantifies errors in paleomagnetic sampling and extends non-parametric regression using neural differential equations.","UC Berkeley  \nUC Berkeley Electronic Theses and Dissertations  \nTitle  \nPhysics-Informed Machine Learning for the Earth Sciences: Applications to Glaciology and Paleomagnetism  \nPermalink  \n[https://escholarship.org/uc/item/7h49z668](https://escholarship.org/uc/item/7h49z668)  \nAuthor  \nSapienza, Facundo Fabián  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nPhysics-Informed Machine Learning for the Earth Sciences: Applications to Glaciology and Paleomagnetism  \nby Facundo Fabián Sapienza  \nA dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in  \nStatistics  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nAssociate Professor Fernando Pérez, Co-chair Professor Jonathan Taylor, Co-chair Assistant Professor Ryan Giordano Assistant Teaching Professor Alexander Strang  \nSpring 2024  \nPhysics-Informed Machine Learning for the Earth Sciences: Applications to Glaciology and Paleomagnetism  \nCopyright 2024  \nby Facundo Fabián Sapienza  \n1  \nAbstract  \nPhysics-Informed Machine Learning for the Earth Sciences: Applications to Glaciology and Paleomagnetism  \nby  \nFacundo Fabián Sapienza  \nDoctor of Philosophy in Statistics  \nUniversity of California, Berkeley  \nAssociate Professor Fernando Pérez, Co-chair Professor Jonathan Taylor, Co-chair  \nThis dissertation studies the application of machine learning in the fields of Glaciology and Paleomagnetism. In the past few years, there have been significant advances in introducing physical constraints in the form of inductive biases in data-driven approaches coming from statistics and machine learning. This gave rise to the field of physics-informed machine learning, which we will introduce in Chapter 1. Chapters 3 and 4 will cover the application of neural differential equations for ice flow modelling, showcasing how the differentiable programming techniques introduced in Chapter 2 have been successfully applied for the inversion and calibration of the internal ice viscosity of mountain glaciers with different climates. This led to the development of ODINN . jl, a multilanguage Julia-Python package for the modelling of global glacier-climate interactions. We will finalize our discussion in Chapter 5 with the quantification of errors involved in paleomagnetic sampling and further applications of non-parametric regression based on neural differential equations.  \ni  \nMany years later as he faced the firing squad,  \nColonel Aureliano Buendía was to remember that distant afternoon when his father took him to discover ice.  \nJosé Arcadio Buendía ventured a murmur:  \n“It’s the largest diamond in the world.”  \n“No,” the gypsy countered. “It’s ice.”  \nFive reales more to touch it,” he said.  \nJosé Arcadio Buendía paid them and put his hand on the ice  \nand held it there for several minutes as his heart  \nfilled with fear and jubilation at the contact with mystery.  \n“This is the great invention of our time.”  \n100 Años de Soledad, Gabriel García Márquez  \nMalchin Peak, in the triple border between Mongolia, Russia, and China. Picture taken a few months before embarking on the doctoral endeavour.  \nii  \nContents  \nContents ii  \nList of Figures vi  \nList of Tables viii  \nIntroduction ix  \nPublications xiii  \nAcknowledgments xvi  \n1 Statistical modelling in the physical sciences 1  \n1.1 Why now? ..................................... 2  \n1.2 Forward and inverse modelling: the language of scientific discovery ...... 7  \n1.2.1 Forward modelling ............................ 7  \n1.2.1.1 The old recipe for physics: differential equations ....... 8  \n1.2.2 Inverse modelling ............................. 9  \n1.2.2.1 The role of differentiable programming ............ 9  \n1.3 Physics-based machine learning ......................... 10  \n1.3.1 Surrogate models and emulators ..............","cbCaikJdjtY5OMKG","https://ap.wps.com/l/cbCaikJdjtY5OMKG","pdf",26887780,1,168,"English","en",105,"# Introduction\n## Publicacions\n## Acknowledgments\n# Statistical modelling in the physical sciences\n## Why now?\n## Forward and inverse modelling\n## Physics-based machine learning\n# Differentiable programming for differential equations\n## Methods: A mathematical perspective\n## Implementation: A computer science perspective","[{\"question\":\"What problem does this dissertation address?\",\"answer\":\"It studies how to apply machine learning to Glaciology and Paleomagnetism by incorporating physical constraints into data-driven models.\"},{\"question\":\"How are differentiable programming and neural differential equations used?\",\"answer\":\"The dissertation uses neural differential equations for ice-flow modelling, and differentiable programming enables inversion and calibration of internal ice viscosity for glaciers under different climates.\"},{\"question\":\"What tools and outcomes are developed for glacier-climate interactions and paleomagnetism?\",\"answer\":\"It develops ODINN . jl for modelling global glacier-climate interactions and quantifies errors in paleomagnetic sampling, extending non-parametric regression via neural differential equations.\"}]","Physics-Informed Machine Learning for the Earth Sciences - Applications to Glaciology and Paleomagnetism - Doctoral Dissertation | PDF",1785722620,423,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"physics-informed-machine-learning-for-the-earth-sciences-applications-to-glaciology-and-paleomagnetism-doctoral-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/physics-informed-machine-learning-for-the-earth-sciences-applications-to-glaciology-and-paleomagnetism-doctoral-dissertation/119134/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this dissertation address?","Question",{"text":75,"@type":76},"It studies how to apply machine learning to Glaciology and Paleomagnetism by incorporating physical constraints into data-driven models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are differentiable programming and neural differential equations used?",{"text":80,"@type":76},"The dissertation uses neural differential equations for ice-flow modelling, and differentiable programming enables inversion and calibration of internal ice viscosity for glaciers under different climates.",{"name":82,"@type":73,"acceptedAnswer":83},"What tools and outcomes are developed for glacier-climate interactions and paleomagnetism?",{"text":84,"@type":76},"It develops ODINN . jl for modelling global glacier-climate interactions and quantifies errors in paleomagnetic sampling, extending non-parametric regression via neural differential equations.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]