{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127510"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127510","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Multi-fidelity machine learning methods for sputtering yield calculations relevant to magnetic fusion energy systems","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","abstract_has_math":false,"creators":["Valaitis, Sonata"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Nuclear, Plasma, Radiolgc Engr","degree_department":null,"school":null,"contributors":["Curreli, Davide","Vergari, Lorenzo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-11","date_published":"2024-12-11","updated_at":"2026-07-22T22:25:04Z","subjects":["Multi-fidelity","Machine Learning","Sputtering","Sputtering Yield","Magnetic Fusion Energy Systems","Tokamaks","Plasma-material Interactions","Gradient Boosting Model","Artificial Neural Network","Yamamura","Feature Engineering","Binary Collision Approximation Simulations","Rustbca","Feature Importance Analysis","Shap","Plasma-facing Components","Tungsten","Boron"],"languages":["en","eng"],"rights":["Copyright 2024 Sonata Valaitis"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127510","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Curreli, Davide","Vergari, Lorenzo"]},{"key":"dc:creator","label":"Author","values":["Valaitis, Sonata"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-11","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Nuclear, Plasma, Radiolgc Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Multi-fidelity","Machine Learning","Sputtering","Sputtering Yield","Magnetic Fusion Energy Systems","Tokamaks","Plasma-material Interactions","Gradient Boosting Model","Artificial Neural Network","Yamamura","Feature Engineering","Binary Collision Approximation Simulations","Rustbca","Feature Importance Analysis","Shap","Plasma-facing Components","Tungsten","Boron"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Sonata Valaitis"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127510"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","The student, Sonata Valaitis, accepted the attached license on 2024-12-06 at 18:46.","The student, Sonata Valaitis, submitted this Thesis for approval on 2024-12-06 at 18:50.","This Thesis was approved for publication on 2024-12-11 at 08:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21539 on 2025-03-28 at 14:57:02","Sputtering of plasma-facing components in magnetic fusion energy systems is an area of significant concern in fusion research. Sputtering yield data in this regime is difficult to obtain both experimentally and computationally. A substantial database of sputtering yields for a range of fusion-relevant materials is systematically generated from empirical formulas, binary collision approximation simulations, and published experimental and calculated results. A machine learning pipeline is optimized for the sputtering yield prediction problem. Linear regression, artificial neural network, and gradient boosting models are assessed in combination with various feature engineering methods. A multi-fidelity gradient boosting tree demonstrates a gain in computational efficiency on the order of 10^6 compared with high-energy binary collision approximation simulations. The gradient boosting model accurately and robustly predicts sputtering yields for a selection of ion materials incident on tungsten and boron across a broad range of ITER-relevant incident ion energies and angles. Feature importance analysis is employed to enhance model interpretability and inform the development of a semi-empirical formula applicable for all angles of ion incidence. Generalizability of the model is assessed for unknown ion and target material parameters. The database and multi-fidelity machine learning model are made available online for web retrieval."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Multi-fidelity machine learning methods for sputtering yield calculations relevant to magnetic fusion energy systems"]}]}],"canonical_facts":{"dc:contributor":["Curreli, Davide","Vergari, Lorenzo"],"dc:creator":["Valaitis, Sonata"],"dc:date":["2024-12-11","2024-12"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","The student, Sonata Valaitis, accepted the attached license on 2024-12-06 at 18:46.","The student, Sonata Valaitis, submitted this Thesis for approval on 2024-12-06 at 18:50.","This Thesis was approved for publication on 2024-12-11 at 08:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21539 on 2025-03-28 at 14:57:02","Sputtering of plasma-facing components in magnetic fusion energy systems is an area of significant concern in fusion research. Sputtering yield data in this regime is difficult to obtain both experimentally and computationally. A substantial database of sputtering yields for a range of fusion-relevant materials is systematically generated from empirical formulas, binary collision approximation simulations, and published experimental and calculated results. A machine learning pipeline is optimized for the sputtering yield prediction problem. Linear regression, artificial neural network, and gradient boosting models are assessed in combination with various feature engineering methods. A multi-fidelity gradient boosting tree demonstrates a gain in computational efficiency on the order of 10^6 compared with high-energy binary collision approximation simulations. The gradient boosting model accurately and robustly predicts sputtering yields for a selection of ion materials incident on tungsten and boron across a broad range of ITER-relevant incident ion energies and angles. Feature importance analysis is employed to enhance model interpretability and inform the development of a semi-empirical formula applicable for all angles of ion incidence. Generalizability of the model is assessed for unknown ion and target material parameters. The database and multi-fidelity machine learning model are made available online for web retrieval."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127510"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Sonata Valaitis"],"dc:subject":["Multi-fidelity","Machine Learning","Sputtering","Sputtering Yield","Magnetic Fusion Energy Systems","Tokamaks","Plasma-material Interactions","Gradient Boosting Model","Artificial Neural Network","Yamamura","Feature Engineering","Binary Collision Approximation Simulations","Rustbca","Feature Importance Analysis","Shap","Plasma-facing Components","Tungsten","Boron"],"dc:title":["Multi-fidelity machine learning methods for sputtering yield calculations relevant to magnetic fusion energy systems"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Nuclear, Plasma, Radiolgc Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}