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Computational uncertainty quantification for some strongly degenerate parabolic convection-diffusion equations.

, and . J. Computational Applied Mathematics, (2019)

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Computational uncertainty quantification for some strongly degenerate parabolic convection-diffusion equations, and . journal of computational and applied mathematics, (2019)Stochastic models for nonlinear convection-dominated flows. Universität Stuttgart, München, Dissertation, (2013)Finite volume methods for conservation laws with noise. Finite volumes for complex applications V, page 527-534. Hoboken, NJ, Wiley, (2008)Finite volume schemes for hyperbolic balance laws with multiplicative noise, and . Appl. Numer. Math., 62 (4): 441--456 (2012)Stochastic Modeling for Heterogeneous Two-Phase Flow, , and . Finite Volumes for Complex Applications VII-Methods and Theoretical Aspects, volume 77 of Springer Proceedings in Mathematics & Statistics, Springer International Publishing, (2014)The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory, , , , , and . Neural networks, 166 (September): 85-104 (2023)Datasets and executables of data-driven uncertainty quantification benchmark in carbon dioxide storage, , , , , , , , , and 1 other author(s). (November 2017)Optimal Exposure Time in Gamma-Ray Attenuation Experiments for Monitoring Time-Dependent Densities, , , , , , , and . Transport in Porous Media, 143 (2): 463-496 (2022)Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario, , , , , , , , , and 1 other author(s). (2018)Hybrid Stochastic Galerkin Finite Volumes for the Diffusively Corrected Lighthill-Whitham-Richards Traffic Model, and . Finite Volumes for Complex Applications VIII - Hyperbolic, Elliptic and Parabolic Problems, 200, page 189-197. Cham, Springer, (2017)