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University of Illinois at Urbana-Champaign
Multi-fidelity machine learning methods for sputtering yield calculations relevant to magnetic fusion energy systems
Abstract
dc:descriptionSubmission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01
Degree
thesis:*- Name thesis:degree_name
- M.S.
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- Nuclear, Plasma, Radiolgc Engr
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2024
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Valaitis, Sonata
- Contributors dc:contributor
-
- Curreli, Davide
- Vergari, Lorenzo
Subjects
dc:subject × 18- 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
Rights
dc:rights- Statement dc:rights
-
- Copyright 2024 Sonata Valaitis
- Language dc:language
- en, eng
Identifiers
dc:identifier.*- Handle dc:identifier
- https://hdl.handle.net/2142/127510
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/127510