Követés
Ionut-Gabriel Farcas
Ionut-Gabriel Farcas
Postdoctoral Associate, Oden Institute for Computational Engineering and Sciences, UT Austin
E-mail megerősítve itt: austin.utexas.edu
Cím
Hivatkozott rá
Hivatkozott rá
Év
Reduced operator inference for nonlinear partial differential equations
E Qian, IG Farcas, K Willcox
SIAM Journal on Scientific Computing 44 (4), A1934-A1959, 2022
392022
Sensitivity-driven adaptive sparse stochastic approximations in plasma microinstability analysis
IG Farcaş, T Görler, HJ Bungartz, F Jenko, T Neckel
Journal of Computational Physics 410, 109394, 2020
202020
Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis
J Konrad, IG Farcaş, B Peherstorfer, A Di Siena, F Jenko, T Neckel, ...
Journal of Computational Physics 451, 110898, 2022
162022
On filtering in non-intrusive data-driven reduced-order modeling
I Farcas, R Munipalli, KE Willcox
AIAA AVIATION 2022 Forum, 3487, 2022
162022
Multilevel Adaptive Sparse Leja Approximations for Bayesian Inverse Problems
IG Farcas, J Latz, E Ullmann, T Neckel, HJ Bungartz
SIAM Journal on Scientific Computing 42 (1), A424–A451, 2020
152020
Multilevel adaptive stochastic collocation with dimensionality reduction
IG Farcaş, PC Sârbu, HJ Bungartz, T Neckel, B Uekermann
Sparse Grids and Applications-Miami 2016, 43-68, 2018
152018
Context-aware model hierarchies for higher-dimensional uncertainty quantification
IG Farcas
Technische Universität München, 2020
142020
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification
IG Farcaș, B Peherstorfer, T Neckel, F Jenko, HJ Bungartz
Computer Methods in Applied Mechanics and Engineering 406, 115908, 2023
92023
A general framework for quantifying uncertainty at scale
IG Farcaş, G Merlo, F Jenko
Communications Engineering 1 (1), 43, 2022
92022
Nonintrusive uncertainty analysis of fluid-structure interaction with spatially adaptive sparse grids and polynomial chaos expansion
IG Farcaș, B Uekermann, T Neckel, HJ Bungartz
SIAM Journal on Scientific Computing 40 (2), B457-B482, 2018
82018
Parametric non-intrusive reduced-order models via operator inference for large-scale rotating detonation engine simulations
I Farcas, R Gundevia, R Munipalli, KE Willcox
AIAA SCITECH 2023 Forum, 0172, 2023
72023
Turbulence suppression by energetic particles: a sensitivity-driven dimension-adaptive sparse grid framework for discharge optimization
IG Farcaş, A Di Siena, F Jenko
Nuclear Fusion 61 (5), 056004, 2021
72021
E-health decision support system for differential diagnosis
R Cucu, C Avram, A Astilean, IG Fărcaş, J Machado
2014 IEEE International Conference on Automation, Quality and Testing …, 2014
62014
Improving the accuracy and scalability of large-scale physics-based data-driven reduced modeling via domain decomposition
IG Farcas, RP Gundevia, R Munipalli, KE Willcox
arXiv preprint arXiv:2311.00883, 2023
32023
High Dimensional Uncertainty Quantification of Fluid-Structure Interaction
IG Farcas
32015
Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine
IG Farcas, RP Gundevia, R Munipalli, KE Willcox
arXiv preprint arXiv:2407.09994, 2024
12024
Learning physics-based reduced models from data for the Hasegawa-Wakatani equations
C Gahr, IG Farcas, F Jenko
arXiv preprint arXiv:2401.05972, 2024
12024
Turbulence suppression by energetic particles: A theoretical framework for discharge optimization
IG Farcas, A Di Siena, F Jenko
arXiv e-prints, arXiv: 2101.03636, 2021
12021
Domain Decomposition for Data-Driven Reduced Modeling of Large-Scale Systems
IG Farcas, RP Gundevia, R Munipalli, KE Willcox
AIAA Journal, 1-16, 2024
2024
Advanced surrogate model for electron-scale turbulence in tokamak pedestals
IG Farcas, G Merlo, F Jenko
arXiv preprint arXiv:2405.09474, 2024
2024
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