3 papers
math.NA2022
Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification
Ionut-Gabriel Farcas, Benjamin Peherstorfer, Tobias Neckel +2
Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for variance reduction to make tractable uncertainty quantification even when numerically simulating t…
stat.CO2019
Multilevel adaptive sparse Leja approximations for Bayesian inverse problems
Ionut-Gabriel Farcas, Jonas Latz, Elisabeth Ullmann +2
Deterministic interpolation and quadrature methods are often unsuitable to address Bayesian inverse problems depending on computationally expensive forward mathematical models. Whi…
physics.comp-ph2018
Sensitivity-driven adaptive sparse stochastic approximations in plasma microinstability analysis
Ionut-Gabriel Farcas, Tobias Görler, Hans-Joachim Bungartz +2
Quantifying uncertainty in predictive simulations for real-world problems is of paramount importance - and far from trivial, mainly due to the large number of stochastic parameters…