Sensitivity-driven adaptive sparse stochastic approximations in plasma microinstability analysis

@article{Farcas2020SensitivitydrivenAS,
  title={Sensitivity-driven adaptive sparse stochastic approximations in plasma microinstability analysis},
  author={Ionut-Gabriel Farcas and Tobias G{\"o}rler and Hans-Joachim Bungartz and Frank Jenko and Tobias Neckel},
  journal={J. Comput. Phys.},
  year={2020},
  volume={410},
  pages={109394}
}
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 and significant computational requirements. Adaptive sparse grid approximations are an established approach to overcome these challenges. However, standard adaptivity is based on global information, thus properties such as lower intrinsic stochastic dimensionality or anisotropic coupling of the input directions… 
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