Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis

DOI10.1016/j.jcp.2021.110898OpenAlexW3137399437WikidataQ114666190 ScholiaQ114666190MaRDI QIDQ2134812

Tobias Neckel, Frank Jenko, Benjamin Peherstorfer, Ionuţ-Gabriel Farcaş, Julia Konrad, Alessandro Di Siena, Hans-Joachim Bungartz

Publication date: 3 May 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.07539

zbMATH Keywords

multi-fidelity Monte Carlo samplingplasma micro-turbulencereduced-dimension low-fidelity modelssensitivity-driven adaptivity

Mathematics Subject Classification ID

Numerical approximation and computational geometry (primarily algorithms) (65Dxx) Probabilistic methods, stochastic differential equations (65Cxx) Approximations and expansions (41Axx)

Related Items

Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization ⋮ A multifidelity Monte Carlo method for realistic computational budgets ⋮ Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification

Uses Software

• GENE
• gs2
• PRMLT

Cites Work

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