Multi-fidelity machine learning methods for sputtering yield calculations relevant to magnetic fusion energy systems
Valaitis, Sonata
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https://hdl.handle.net/2142/127510
Description
Title
Multi-fidelity machine learning methods for sputtering yield calculations relevant to magnetic fusion energy systems
Author(s)
Valaitis, Sonata
Issue Date
2024-12-11
Director of Research (if dissertation) or Advisor (if thesis)
Curreli, Davide
Committee Member(s)
Vergari, Lorenzo
Department of Study
Nuclear, Plasma, & Rad Engr
Discipline
Nuclear, Plasma, Radiolgc Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
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
Language
eng
Abstract
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.
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