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

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

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

Journal Article · Wed Jun 01 00:00:00 EDT 2022 · Computer Physics Communications

DOI:https://doi.org/10.1016/j.cpc.2022.108436· OSTI ID:1890337

^[1]; Mustafa, Mohammad ^[2]; Curreli, Davide ^[2]; ^[1]; ^[1]; ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Univ. of Illinois at Urbana-Champaign, IL (United States)

In this work, 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.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC); USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: AC05-00OR22725; SC0018141

OSTI ID:: 1890337

Alternate ID(s):: OSTI ID: 1874063

Journal Information:: Computer Physics Communications, Journal Name: Computer Physics Communications Vol. 279; ISSN 0010-4655

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (32)

Energy Dependence of Ion-Induced Sputtering Yields from Monatomic Solids at Normal Incidence Yamamura, Yasunori; Tawara, Hiro Atomic Data and Nuclear Data Tables, Vol. 62, Issue 2 https://doi.org/10.1006/adnd.1996.0005	journal	March 1996
Adaptive sparse grid multilevel methods for elliptic PDEs based on finite differences Griebel, Michael Computing, Vol. 61, Issue 2 https://doi.org/10.1007/BF02684411	journal	June 1998
Convergence of quasi-optimal sparse-grid approximation of Hilbert-space-valued functions: application to random elliptic PDEs Nobile, F.; Tamellini, L.; Tempone, R. Numerische Mathematik, Vol. 134, Issue 2 https://doi.org/10.1007/s00211-015-0773-y	journal	October 2015
Analysis of quasi-optimal polynomial approximations for parameterized PDEs with deterministic and stochastic coefficients Tran, Hoang; Webster, Clayton G.; Zhang, Guannan Numerische Mathematik, Vol. 137, Issue 2 https://doi.org/10.1007/s00211-017-0878-6	journal	March 2017
High-Dimensional Adaptive Sparse Polynomial Interpolation and Applications to Parametric PDEs Chkifa, Abdellah; Cohen, Albert; Schwab, Christoph Foundations of Computational Mathematics, Vol. 14, Issue 4 https://doi.org/10.1007/s10208-013-9154-z	journal	May 2013
The behaviour of hydrogen atoms implanted into metals Möller, W. Nuclear Instruments and Methods in Physics Research, Vol. 209-210 https://doi.org/10.1016/0167-5087(83)90882-7	journal	May 1983
A dynamically adaptive sparse grids method for quasi-optimal interpolation of multidimensional functions Stoyanov, Miroslav K.; Webster, Clayton G. Computers & Mathematics with Applications, Vol. 71, Issue 11 https://doi.org/10.1016/j.camwa.2015.12.045	journal	June 2016
Vlasov simulations of plasma-wall interactions in a weakly collisional plasma Manfredi, G.; Valsaque, F. Computer Physics Communications, Vol. 164, Issue 1-3 https://doi.org/10.1016/j.cpc.2004.06.037	journal	December 2004
hPIC: A scalable electrostatic Particle-in-Cell for Plasma–Material Interactions Khaziev, Rinat; Curreli, Davide Computer Physics Communications, Vol. 229 https://doi.org/10.1016/j.cpc.2018.03.028	journal	August 2018
Global sensitivity analysis on numerical solver parameters of Particle-In-Cell models in particle accelerator systems Frey, Matthias; Adelmann, Andreas Computer Physics Communications, Vol. 258 https://doi.org/10.1016/j.cpc.2020.107577	journal	January 2021
Sensitivity analysis of environmental models: A systematic review with practical workflow Pianosi, Francesca; Beven, Keith; Freer, Jim Environmental Modelling & Software, Vol. 79 https://doi.org/10.1016/j.envsoft.2016.02.008	journal	May 2016
On the Lebesgue constant of Leja sequences for the complex unit disk and of their real projection Chkifa, Moulay Abdellah Journal of Approximation Theory, Vol. 166 https://doi.org/10.1016/j.jat.2012.11.005	journal	February 2013
An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations Ma, Xiang; Zabaras, Nicholas Journal of Computational Physics, Vol. 228, Issue 8 https://doi.org/10.1016/j.jcp.2009.01.006	journal	May 2009
F-TRIDYN: A Binary Collision Approximation code for simulating ion interactions with rough surfaces Drobny, Jon; Hayes, Alyssa; Curreli, Davide Journal of Nuclear Materials, Vol. 494 https://doi.org/10.1016/j.jnucmat.2017.07.037	journal	October 2017
Radial Basis Functions Buhmann, Martin D. Cambridge University Press https://doi.org/10.1017/CBO9780511543241	book	January 2009
Radial basis functions Buhmann, M. D. Acta Numerica, Vol. 9 https://doi.org/10.1017/S0962492900000015	journal	January 2000
Sparse grids Bungartz, Hans-Joachim; Griebel, Michael Acta Numerica, Vol. 13 https://doi.org/10.1017/S0962492904000182	journal	May 2004
Neural network approximation DeVore, Ronald; Hanin, Boris; Petrova, Guergana Acta Numerica, Vol. 30 https://doi.org/10.1017/S0962492921000052	journal	May 2021
Least Squares Support Vector Machine Classifiers Suykens, J. A. K.; Vandewalle, J. Neural Processing Letters, Vol. 9, Issue 3, p. 293-300 https://doi.org/10.1023/A:1018628609742	journal	June 1999
A tutorial on support vector regression Smola, Alex J.; Schölkopf, Bernhard Statistics and Computing, Vol. 14, Issue 3 https://doi.org/10.1023/B:STCO.0000035301.49549.88	journal	August 2004
Ion energy-angle distribution functions at the plasma-material interface in oblique magnetic fields Khaziev, Rinat; Curreli, Davide Physics of Plasmas, Vol. 22, Issue 4 https://doi.org/10.1063/1.4916910	journal	April 2015
Plasma–wall transition in an oblique magnetic field Chodura, R. Physics of Fluids, Vol. 25, Issue 9 https://doi.org/10.1063/1.863955	journal	January 1982
Trajectories of charged particles traversing a plasma sheath in an oblique magnetic field DeWald, A. B.; Bailey, A. W.; Brooks, J. N. Physics of Fluids, Vol. 30, Issue 1 https://doi.org/10.1063/1.866185	journal	January 1987
Theory of the collisional presheath in an oblique magnetic field Riemann, K. ‐U. Physics of Plasmas, Vol. 1, Issue 3 https://doi.org/10.1063/1.870800	journal	March 1994
Plasma-material interactions in current tokamaks and their implications for next step fusion reactors Federici, G.; Skinner, C. H.; Brooks, J. N. Nuclear Fusion, Vol. 41, Issue 12 https://doi.org/10.1088/0029-5515/41/12/218	journal	December 2001
Magnetized plasma–wall transition—consequences for wall sputtering and erosion Devaux, S.; Manfredi, G. Plasma Physics and Controlled Fusion, Vol. 50, Issue 2 https://doi.org/10.1088/0741-3335/50/2/025009	journal	January 2008
Plasma–wall interaction studies within the EUROfusion consortium: progress on plasma-facing components development and qualification Brezinsek, S.; Coenen, J. W.; Schwarz-Selinger, T. Nuclear Fusion, Vol. 57, Issue 11 https://doi.org/10.1088/1741-4326/aa796e	journal	August 2017
An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data Nobile, F.; Tempone, R.; Webster, C. G. SIAM Journal on Numerical Analysis, Vol. 46, Issue 5 https://doi.org/10.1137/070680540	journal	January 2008
Numerical Analysis of Spectral Methods: Theory and Applications Gottlieb, David; Orszag, Steven A. CBMS-NSF Regional Conference Series in Applied Mathematics https://doi.org/10.1137/1.9781611970425	book	January 1977
A Method for Dimensionally Adaptive Sparse Trigonometric Interpolation of Periodic Functions Morrow, Zachary; Stoyanov, Miroslav SIAM Journal on Scientific Computing, Vol. 42, Issue 4 https://doi.org/10.1137/19M1283483	journal	January 2020
Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator Joe, Stephen; Kuo, Frances Y. ACM Transactions on Mathematical Software, Vol. 29, Issue 1 https://doi.org/10.1145/641876.641879	journal	March 2003
RustBCA: A High-Performance Binary-Collision-Approximation Code for Ion-Material Interactions Drobny, Jon.; Curreli, Davide Journal of Open Source Software, Vol. 6, Issue 64 https://doi.org/10.21105/joss.03298	journal	August 2021

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