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ORCIDAndrea Barth
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2020 – today
- 2025
[j10]Andreas Stein
, Andrea Barth:
Stochastic transport with Lévy noise fully discrete numerical approximation. Math. Comput. Simul. 227: 347-370 (2025)- 2024
- [i9]Fabio Musco, Andrea Barth:
Deep learning methods for stochastic Galerkin approximations of elliptic random PDEs. CoRR abs/2409.08063 (2024) - 2023
- [i8]Cedric Aaron Beschle, Andrea Barth:
Quasi continuous level Monte Carlo for random elliptic PDEs. CoRR abs/2303.08694 (2023) - [i7]Cedric Aaron Beschle, Andrea Barth:
Quasi continuous level Monte Carlo. CoRR abs/2305.15949 (2023) - 2022
- [j9]David Hägele, Christoph Schulz, Cedric Beschle, Hannah Booth, Miriam Butt, Andrea Barth, Oliver Deussen, Daniel Weiskopf:
Uncertainty visualization: Fundamentals and recent developments. it Inf. Technol. 64(4-5): 121-132 (2022) - 2021
- [c2]Lukas Mehl, Cedric Beschle, Andrea Barth, Andrés Bruhn:
An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation. SSVM 2021: 140-152 - [i6]Lukas Brencher, Andrea Barth:
Scalar conservation laws with stochastic discontinuous flux function. CoRR abs/2107.00549 (2021) - [i5]Andrea Barth, Robin Merkle:
Multilevel Monte Carlo estimators for elliptic PDEs with Lévy-type diffusion coefficient. CoRR abs/2108.05604 (2021) - 2020
- [i4]Andrea Barth, Robin Merkle:
Subordinated Gaussian Random Fields in Elliptic Partial Differential Equations. CoRR abs/2011.09311 (2020)
2010 – 2019
- 2019
- [j8]Kevin Carlberg, Lukas Brencher, Bernard Haasdonk, Andrea Barth:
Data-Driven Time Parallelism via Forecasting. SIAM J. Sci. Comput. 41(3): B466-B496 (2019) - [i3]Andrea Barth, Andreas Stein:
A stochastic transport problem with Lévy noise: Fully discrete numerical approximation. CoRR abs/1910.14657 (2019) - 2018
- [j7]Andrea Barth, Andreas Stein:
A Study of Elliptic Partial Differential Equations with Jump Diffusion Coefficients. SIAM/ASA J. Uncertain. Quantification 6(4): 1707-1743 (2018) - [j6]Andrea Barth, Tobias Stüwe:
Weak convergence of Galerkin approximations of stochastic partial differential equations driven by additive Lévy noise. Math. Comput. Simul. 143: 215-225 (2018) - [i2]Markus Köppel, Fabian Franzelin, Ilja Kröker, Sergey Oladyshkin, Gabriele Santin, Dominik Wittwar, Andrea Barth, Bernard Haasdonk, Wolfgang Nowak, Dirk Pflüger, Christian Rohde:
Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario. CoRR abs/1802.03064 (2018) - 2016
- [j5]Andrea Barth, Raimund Bürger, Ilja Kröker, Christian Rohde:
Computational uncertainty quantification for a clarifier-thickener model with several random perturbations: A hybrid stochastic Galerkin approach. Comput. Chem. Eng. 89: 11-26 (2016) - [j4]Andrea Barth, Franz G. Fuchs:
Uncertainty Quantification for Hyperbolic Conservation Laws with Flux Coefficients Given by Spatiotemporal Random Fields. SIAM J. Sci. Comput. 38(4) (2016) - [i1]Kevin Carlberg, Lukas Brencher, Bernard Haasdonk, Andrea Barth:
Data-driven time parallelism via forecasting. CoRR abs/1610.09049 (2016) - 2014
- [c1]Andrea Barth, Christoph Schwab, Jonas Sukys:
Multilevel Monte Carlo Simulation of Statistical Solutions to the Navier-Stokes Equations. MCQMC 2014: 209-227 - 2013
- [j3]Assyr Abdulle, Andrea Barth, Christoph Schwab:
Multilevel Monte Carlo Methods for Stochastic Elliptic Multiscale PDEs. Multiscale Model. Simul. 11(4): 1033-1070 (2013) - 2012
- [j2]Andrea Barth, Annika Lang:
Multilevel Monte Carlo method with applications to stochastic partial differential equations. Int. J. Comput. Math. 89(18): 2479-2498 (2012) - 2011
- [j1]Andrea Barth, Christoph Schwab, Nathaniel Zollinger:
Multi-level Monte Carlo Finite Element method for elliptic PDEs with stochastic coefficients. Numerische Mathematik 119(1): 123-161 (2011)
Coauthor Index
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