2026-09-20T17:23:13Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/127510 2026-02-03 col_2142_5131 col_2142_14837 com_2142_5130 com_2142_14836 com_2142_234

application/pdf en text 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 Multi-fidelity machine learning methods for sputtering yield calculations relevant to magnetic fusion energy systems Valaitis, Sonata 2024-12-11 Curreli, Davide Vergari, Lorenzo 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 eng 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. 2024-12 Thesis https://hdl.handle.net/2142/127510 Copyright 2024 Sonata Valaitis Nuclear, Plasma, & Rad Engr Nuclear, Plasma, Radiolgc Engr University of Illinois at Urbana-Champaign M.S. Thesis