2022
- D. Hägele et al., “Uncertainty Visualization: Fundamentals and Recent Developments,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: 10.1515/itit-2022-0033.
- L. Mehl, C. Beschle, A. Barth, and A. Bruhn, “Replication Data for: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation,” 2022, doi: 10.18419/darus-2890.
2021
- L. Brencher and A. Barth, “Stochastic conservation laws with discontinuous flux functions: The multidimensional case,” 2021.
- L. Brencher and A. Barth, “Scalar conservation laws with stochastic discontinuous flux function,” ArXiv e-prints, arXiv:2107.00549 math.NA, 2021.
- L. Mehl, C. Beschle, A. Barth, and A. Bruhn, “An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation,” Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), pp. 140--152, 2021, doi: 10.1007/978-3-030-75549-2_12.
2020
- L. Brencher and A. Barth, “Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions,” in International Conference on Finite Volumes for Complex Applications, in International Conference on Finite Volumes for Complex Applications. Springer, 2020, pp. 265--273.
2019
- K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-driven time parallelism via forecasting,” SIAM Journal on Scientific Computing, vol. 41, no. 3, Art. no. 3, 2019.
- M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario,” Computational Geosciences, vol. 23, no. 2, Art. no. 2, Apr. 2019, doi: 10.1007/s10596-018-9785-x.
2018
- A. Barth and A. Stein, “A Study of Elliptic Partial Differential Equations with Jump Diffusion Coefficients,” SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, vol. 6, no. 4, Art. no. 4, 2018, doi: 10.1137/17M1148888.
- A. Barth and A. Stein, “Approximation and simulation of infinite-dimensional Levy processes,” STOCHASTICS AND PARTIAL DIFFERENTIAL EQUATIONS-ANALYSIS AND COMPUTATIONS, vol. 6, no. 2, Art. no. 2, Jun. 2018, doi: 10.1007/s40072-017-0109-2.
- A. Barth and T. Stüwe, “Weak convergence of Galerkin approximations of stochastic partial differential equations driven by additive Lévy noise,” Math. Comput. Simulation, vol. 143, pp. 215--225, 2018, [Online]. Available: https://doi.org/10.1016/j.matcom.2017.03.007
2017
- A. Barth and F. G. Fuchs, “Uncertainty quantification for linear hyperbolic equations with stochastic process or random field coefficients,” APPLIED NUMERICAL MATHEMATICS, vol. 121, pp. 38–51, Nov. 2017, doi: 10.1016/j.apnum.2017.06.009.
- A. Barth, B. Harrach, N. Hyvoenen, and L. Mustonen, “Detecting stochastic inclusions in electrical impedance tomography,” INVERSE PROBLEMS, vol. 33, no. 11, Art. no. 11, Nov. 2017, doi: 10.1088/1361-6420/aa8f5c.
- A. Barth, B. Harrach, N. Hyvönen, and L. Mustonen, “Detecting stochastic inclusions in electrical impedance tomography,” Inv. Prob., vol. 33, no. 11, Art. no. 11, 2017, [Online]. Available: http://arxiv.org/abs/1706.03962
- A. Barth and A. Stein, “A study of elliptic partial differential equations with jump diffusion coefficients,” 2017.
- M. Köppel et al., “Datasets and executables of data-driven uncertainty quantification benchmark in carbon dioxide storage.” Nov. 2017. doi: 10.5281/zenodo.933827.
2016
- A. Barth, R. Bürger, I. Kröker, and C. Rohde, “Computational uncertainty quantification for a clarifier-thickener model with several random perturbations: A hybrid stochastic Galerkin approach,” Computers & Chemical Engineering, vol. 89, pp. 11-- 26, 2016, doi: http://dx.doi.org/10.1016/j.compchemeng.2016.02.016.
- A. Barth and F. G. Fuchs, “Uncertainty quantification for hyperbolic conservation laws with flux coefficients given by spatiotemporal random fields,” SIAM J. Sci. Comput., vol. 38, no. 4, Art. no. 4, 2016, doi: 10.1137/15M1027723.
- A. Barth and I. Kröker, “Finite volume methods for hyperbolic partial differential equations with spatial noise,” in Springer Proceedings in Mathematics and Statistics, in Springer Proceedings in Mathematics and Statistics, vol. submitted. Springer International Publishing, 2016.
- A. Barth, S. Moreno-Bromberg, and O. Reichmann, “A Non-stationary Model of Dividend Distribution in a Stochastic Interest-Rate Setting,” Comp. Economics, vol. 47, no. 3, Art. no. 3, 2016, doi: 10.1007/s10614-015-9502-y.
- A. Barth, C. Schwab, and J. Sukys, “Multilevel Monte Carlo simulation of statistical solutions to the Navier-Stokes equations,” in Monte Carlo and quasi-Monte Carlo methods, in Monte Carlo and quasi-Monte Carlo methods, vol. 163. Springer, Cham, 2016, pp. 209--227. doi: 10.1007/978-3-319-33507-0_8.
- A. Barth and A. Stein, “Approximation and simulation of infinite-dimensional Lévy processes,” 2016. [Online]. Available: http://arxiv.org/abs/1612.05541
2014
- A. Barth and F. E. Benth, “The forward dynamics in energy markets -- infinite-dimensional modelling and simulation,” Stochastics, vol. 86, no. 6, Art. no. 6, 2014, doi: 10.1080/17442508.2014.895359.
- A. Barth and S. Moreno-Bromberg, “Optimal risk and liquidity management with costly refinancing opportunities,” Insurance Math. Econom., vol. 57, pp. 31--45, 2014, doi: 10.1016/j.insmatheco.2014.05.001.
2013
- A. Abdulle, A. Barth, and C. Schwab, “Multilevel Monte Carlo methods for stochastic elliptic multiscale PDEs,” Multiscale Model. Simul., vol. 11, no. 4, Art. no. 4, 2013, doi: 10.1137/120894725.
- A. Barth and A. Lang, “L^p and almost sure convergence of a Milstein scheme for stochastic partial differential equations,” Stochastic Process. Appl., vol. 123, no. 5, Art. no. 5, 2013, doi: 10.1016/j.spa.2013.01.003.
- A. Barth, A. Lang, and C. Schwab, “Multilevel Monte Carlo method for parabolic stochastic partial differential equations,” BIT, vol. 53, no. 1, Art. no. 1, 2013, doi: 10.1007/s10543-012-0401-5.
2012
- A. Barth and A. Lang, “Simulation of stochastic partial differential equations using finite element methods,” Stochastics, vol. 84, no. 2–3, Art. no. 2–3, 2012, doi: 10.1080/17442508.2010.523466.
- A. Barth and A. Lang, “Milstein approximation for advection-diffusion equations driven by multiplicative noncontinuous martingale noises,” Appl. Math. Optim., vol. 66, no. 3, Art. no. 3, 2012, doi: 10.1007/s00245-012-9176-y.
- A. Barth and A. Lang, “Multilevel Monte Carlo method with applications to stochastic partial differential equations,” Int. J. Comput. Math., vol. 89, no. 18, Art. no. 18, 2012, doi: 10.1080/00207160.2012.701735.
2011
- A. Barth, F. E. Benth, and J. Potthoff, “Hedging of spatial temperature risk with market-traded futures,” Appl. Math. Finance, vol. 18, no. 2, Art. no. 2, 2011, doi: 10.1080/13504861003722385.
- A. Barth, C. Schwab, and N. Zollinger, “Multi-level Monte Carlo finite element method for elliptic PDEs with stochastic coefficients,” Numer. Math., vol. 119, no. 1, Art. no. 1, 2011, doi: 10.1007/s00211-011-0377-0.
2010
- A. Barth, “A finite element method for martingale-driven stochastic partial differential equations,” Commun. Stoch. Anal., vol. 4, no. 3, Art. no. 3, 2010, [Online]. Available: https://www.math.lsu.edu/cosa/4-3-04209.pdf
2009
- A. Barth, “Stochastic Partial Differential Equations: Approximations and Applications,” University of Oslo, CMA, 2009. [Online]. Available: https://www.duo.uio.no/handle/10852/10669
2006
- A. Barth, “Distribution of the First Rendezvous Time of Two Geometric Brownian Motions,” 2006.
Contact

Andrea Barth
Prof. Dr.Head of Group