Data-driven surrogate modeling of hPIC ion energy-angle distributions for high-dimensional sensitivity analysis of plasma parameters' uncertainty

Pablo Seleson; Mohammad Mustafa; Davide Curreli; Cory D. Hauck; Miroslav Stoyanov; David E. Bernholdt

doi:10.1016/j.cpc.2022.108436

Data-driven surrogate modeling of hPIC ion energy-angle distributions for high-dimensional sensitivity analysis of plasma parameters' uncertainty

Pablo Seleson, Mohammad Mustafa, Davide Curreli, Cory D. Hauck, Miroslav Stoyanov, David E. Bernholdt

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

We present a data-driven strategy for effective construction of a surrogate model in high-dimensional parameter space for the ion energy-angle distribution (IEAD) output of hPIC simulations of plasma-surface interactions. The methodology is based on a bin-by-bin least-squares fitting of the IEAD in the parameter space. The fitting is performed in a transformed coordinate system to normalize the IEAD, and it employs sparse grids for sampling the parameter space to overcome sampling challenges in high dimensions. The surrogate model is significantly cheaper computationally than direct hPIC simulations yet maintains high fidelity to them, providing a fast emulator for hPIC simulations. Sensitivity analysis based on the surrogate model is utilized to characterize the dependence of the ion impact angle and energy moments on the physical parameters.¹1

Original language	English
Article number	108436
Journal	Computer Physics Communications
Volume	279
DOIs	https://doi.org/10.1016/j.cpc.2022.108436
State	Published - Oct 2022

Funding

This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This material is based upon work supported by the U.S. Department of Energy, Office of Science , Office of Fusion Energy Sciences and Office of Advanced Scientific Computing Research through the Scientific Discovery through Advanced Computing (SciDAC) project on Plasma-Surface Interactions (work at University of Illinois was supported under Award No. DE-SC0018141 ). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. A MATLAB code to construct the surrogate models from the hPIC data and perform sensitivity analysis can be downloaded from: https://github.com/pabloseleson/hPIC-surrogate . The dataset for this publication can be freely accessed online through Constellation (doi: https://doi.org/10.13139/ORNLNCCS/1846780 ).

Keywords

Data-driven
Plasma physics
Sensitivity analysis
Sparse grids
Surrogate modeling
Uncertainty quantification

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10.1016/j.cpc.2022.108436

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title = "Data-driven surrogate modeling of hPIC ion energy-angle distributions for high-dimensional sensitivity analysis of plasma parameters' uncertainty",

abstract = "We present a data-driven strategy for effective construction of a surrogate model in high-dimensional parameter space for the ion energy-angle distribution (IEAD) output of hPIC simulations of plasma-surface interactions. The methodology is based on a bin-by-bin least-squares fitting of the IEAD in the parameter space. The fitting is performed in a transformed coordinate system to normalize the IEAD, and it employs sparse grids for sampling the parameter space to overcome sampling challenges in high dimensions. The surrogate model is significantly cheaper computationally than direct hPIC simulations yet maintains high fidelity to them, providing a fast emulator for hPIC simulations. Sensitivity analysis based on the surrogate model is utilized to characterize the dependence of the ion impact angle and energy moments on the physical parameters.11",

keywords = "Data-driven, Plasma physics, Sensitivity analysis, Sparse grids, Surrogate modeling, Uncertainty quantification",

author = "Pablo Seleson and Mohammad Mustafa and Davide Curreli and Hauck, {Cory D.} and Miroslav Stoyanov and Bernholdt, {David E.}",

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T1 - Data-driven surrogate modeling of hPIC ion energy-angle distributions for high-dimensional sensitivity analysis of plasma parameters' uncertainty

AU - Seleson, Pablo

AU - Mustafa, Mohammad

AU - Curreli, Davide

AU - Hauck, Cory D.

AU - Stoyanov, Miroslav

AU - Bernholdt, David E.

PY - 2022/10

Y1 - 2022/10

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AB - We present a data-driven strategy for effective construction of a surrogate model in high-dimensional parameter space for the ion energy-angle distribution (IEAD) output of hPIC simulations of plasma-surface interactions. The methodology is based on a bin-by-bin least-squares fitting of the IEAD in the parameter space. The fitting is performed in a transformed coordinate system to normalize the IEAD, and it employs sparse grids for sampling the parameter space to overcome sampling challenges in high dimensions. The surrogate model is significantly cheaper computationally than direct hPIC simulations yet maintains high fidelity to them, providing a fast emulator for hPIC simulations. Sensitivity analysis based on the surrogate model is utilized to characterize the dependence of the ion impact angle and energy moments on the physical parameters.11

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Data-driven surrogate modeling of hPIC ion energy-angle distributions for high-dimensional sensitivity analysis of plasma parameters' uncertainty

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