| Reduced operator inference for nonlinear partial differential equations E Qian, IG Farcas, K Willcox SIAM Journal on Scientific Computing 44 (4), A1934-A1959, 2022 | 34 | 2022 |
| 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 | 18 | 2020 |
| 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 | 16 | 2022 |
| 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 | 14 | 2020 |
| Context-aware model hierarchies for higher-dimensional uncertainty quantification IG Farcas Technische Universität München, 2020 | 14 | 2020 |
| 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 | 14 | 2018 |
| On filtering in non-intrusive data-driven reduced-order modeling I Farcas, R Munipalli, KE Willcox AIAA AVIATION 2022 Forum, 3487, 2022 | 13 | 2022 |
| A general framework for quantifying uncertainty at scale IG Farcaş, G Merlo, F Jenko Communications Engineering 1 (1), 43, 2022 | 9 | 2022 |
| 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 | 8 | 2023 |
| 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 | 8 | 2018 |
| 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 | 7 | 2021 |
| 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 | 6 | 2014 |
| 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 | 5 | 2023 |
| High Dimensional Uncertainty Quantification of Fluid-Structure Interaction IG Farcas | 3 | 2015 |
| 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 | 2 | 2023 |
| Learning physics-based reduced models from data for the Hasegawa-Wakatani equations C Gahr, IG Farcas, F Jenko arXiv preprint arXiv:2401.05972, 2024 | 1 | 2024 |
| 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 | 1 | 2021 |
| Advanced surrogate model for electron-scale turbulence in tokamak pedestals IG Farcas, G Merlo, F Jenko arXiv preprint arXiv:2405.09474, 2024 | | 2024 |
| Comparison of numerical methods in uncertainty quantification IG Farcas Gesellschaft für Informatik eV, 2014 | | 2014 |
