Quantification of Uncertainty: Improving Efficiency and Technology: QUIET selected contributions (Lecture Notes in Computational Science and Engineering)

1. Adeli, E. et al., Effect of Load Path on Parameter Identification for Plasticity Models using Bayesian Methods.- 2. Brugiapaglia S., A compressive spectral collocation method for the diffusion equation under the restricted isometry property.- 3. D'Elia, M. et al., Surrogate-based Ensemble Grouping Strategies for Embedded Sampling-based Uncertainty Quantification.- 4. Afkham, B.M. et al., Conservative Model Order Reduction for Fluid Flow.- 5. Clark C.L. and Winter C.L., A Semi-Markov Model of Mass Transport through Highly Heterogeneous Conductivity Fields.- 6. Matthies, H.G., Analysis of Probabilistic and Parametric Reduced Order Models.- 7. Carraturo, M. et al., Reduced Order Isogeometric Analysis Approach for PDEs in Parametrized Domains.- 8. Boccadifuoco, A. et al., Uncertainty quantification applied to hemodynamic simulations of thoracic aorta aneurysms: sensitivity to inlet conditions.- 9. Anderlini, A.et al., Cavitation model parameter calibration for simulations of three-phase injector flows.- 10. Hijazi, S. et al., Non-Intrusive Polynomial Chaos Method Applied to Full-Order and Reduced Problems in Computational Fluid Dynamics: a Comparison and Perspectives.- 11. Bult, M. et al., A practical example for the non-linear Bayesian filtering of model parameters.