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

, , , , , , and . J. Comput. Phys., (2022)

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Hyperscaling of plasma turbulence simulations in DEISA., , , , and . CLADE@HPDC, page 19-26. ACM, (2007)Block-structured grids in full velocity space for Eulerian gyrokinetic simulations., , , , , and . Comput. Phys. Commun., (2017)EXAHD: An Exa-scalable Two-Level Sparse Grid Approach for Higher-Dimensional Problems in Plasma Physics and Beyond., , , , , , , , and . Euro-Par Workshops (2), volume 8806 of Lecture Notes in Computer Science, page 565-576. Springer, (2014)A parallel implementation of an MHD code for the simulation of mechanically driven, turbulent dynamos in spherical geometry., , , and . Comput. Phys. Commun., 179 (4): 245-249 (2008)EXAHD: A massively parallel fault tolerant sparse grid approach for high-dimensional turbulent plasma simulations, , , , , , , , and . Software for Exascale Computing-SPPEXA 2016-2019, page 301--329. Springer International Publishing, (2020)Scientific data interpolation with low dimensional manifold model., , , , , and . J. Comput. Physics, (2018)Sensitivity-driven adaptive sparse stochastic approximations in plasma microinstability analysis., , , , and . CoRR, (2018)On the role of numerical dissipation in gyrokinetic Vlasov simulations of plasma microturbulence., , and . Comput. Phys. Commun., 181 (8): 1428-1437 (2010)Towards optimal explicit time-stepping schemes for the gyrokinetic equations., and . Comput. Phys. Commun., 185 (7): 1938-1946 (2014)The global version of the gyrokinetic turbulence code GENE., , , , , , and . J. Comput. Physics, 230 (18): 7053-7071 (2011)