Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Ionuţ-Gabriel Farcaş"'
Publikováno v:
Communications Engineering
In many fields of science, comprehensive and realistic computational models are available nowadays. Often, the respective numerical calculations call for the use of powerful supercomputers, and therefore only a limited number of cases can be investig
Autor:
Julia Konrad, Ionuţ-Gabriel Farcaş, Benjamin Peherstorfer, Alessandro Di Siena, Frank Jenko, Tobias Neckel, Hans-Joachim Bungartz
Publikováno v:
Journal of Computational Physics
The linear micro-instabilities driving turbulent transport in magnetized fusion plasmas (as well as the respective nonlinear saturation mechanisms) are known to be sensitive with respect to various physical parameters characterizing the background pl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0d48c9233c4dc86c6e075c04284bde1
https://hdl.handle.net/21.11116/0000-0009-EF8A-F21.11116/0000-0009-EF88-1
https://hdl.handle.net/21.11116/0000-0009-EF8A-F21.11116/0000-0009-EF88-1
We present a new scientific machine learning method that learns from data a computationally inexpensive surrogate model for predicting the evolution of a system governed by a time-dependent nonlinear partial differential equation (PDE), an enabling t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a0e2e316ba94e90337445a34c2fdcf7
Autor:
Tobias Neckel, Paul Cristian Sârbu, Hans-Joachim Bungartz, Ionuţ-Gabriel Farcaş, Benjamin Uekermann
Publikováno v:
Lecture Notes in Computational Science and Engineering ISBN: 9783319754253
We present a multilevel stochastic collocation (MLSC) with a dimensionality reduction approach to quantify the uncertainty in computationally intensive applications. Standard MLSC typically employs grids with predetermined resolutions. Even more, sto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5bbb67ed42452f095cb7c08a4873c81
https://doi.org/10.1007/978-3-319-75426-0_3
https://doi.org/10.1007/978-3-319-75426-0_3