Scientific machine learning based reduced-order models for plasma turbulence simulations

dc.contributor.authorGahr, Constantinen
dc.contributor.authorFarcas, Ionut-Gabrielen
dc.contributor.authorJenko, Franken
dc.date.accessioned2025-02-10T19:37:02Zen
dc.date.available2025-02-10T19:37:02Zen
dc.date.issued2024-11-18en
dc.description.abstractThis paper investigates non-intrusive Scientific Machine Learning (SciML) Reduced-Order Models (ROMs) for plasma turbulence simulations. In particular, we focus on Operator Inference (OpInf) to build low-cost physics-based ROMs from data for such simulations. As a representative example, we consider the (classical) Hasegawa-Wakatani (HW) equations used for modeling two-dimensional electrostatic drift-wave turbulence. For a comprehensive perspective of the potential of OpInf to construct predictive ROMs, we consider three setups for the HW equations by varying a key parameter, namely, the adiabaticity coefficient. These setups lead to the formation of complex and nonlinear dynamics, which makes the construction of predictive ROMs of any kind challenging. We generate the training datasets by performing direct numerical simulations of the HW equations and recording the computed state data and outputs over a time horizon of 100 time units in the turbulent phase. We then use these datasets to construct OpInf ROMs for predictions over 400 additional time units, that is, 400 % more than the training horizon. Our results show that the OpInf ROMs capture important statistical features of the turbulent dynamics and generalize beyond the training time horizon while reducing the computational effort of the high-fidelity simulation by up to five orders of magnitude. In the broader context of fusion research, this shows that non-intrusive SciML ROMs have the potential to drastically accelerate numerical studies, which can ultimately enable tasks such as the design of optimized fusion devices.en
dc.description.versionPublished versionen
dc.format.extent14 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN 113904 (Article number)en
dc.identifier.doihttps://doi.org/10.1063/5.0225584en
dc.identifier.eissn1089-7674en
dc.identifier.issn1070-664Xen
dc.identifier.issue11en
dc.identifier.urihttps://hdl.handle.net/10919/124555en
dc.identifier.volume31en
dc.language.isoenen
dc.publisherAIP Publishingen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleScientific machine learning based reduced-order models for plasma turbulence simulationsen
dc.title.serialPhysics of Plasmasen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Scienceen
pubs.organisational-groupVirginia Tech/Science/Mathematicsen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Science/COS T&R Facultyen

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