Publications

List of publications of the Research Group

  1. 2022

    1. 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.
    2. 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.

  2. 2021

    1. L. Brencher and A. Barth, “Scalar conservation laws with stochastic discontinuous flux function,” ArXiv e-prints, arXiv:2107.00549 math.NA, 2021.
    2. L. Brencher and A. Barth, “Stochastic conservation laws with discontinuous flux functions: The multidimensional case,” 2021.
    3. 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.

  3. 2020

    1. L. Brencher and A. Barth, “Hyperbolic Conservation Laws with Stochastic Discontinuous Flux Functions,” in International Conference on Finite Volumes for Complex Applications, 2020, pp. 265--273.

  4. 2019

    1. 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.
    2. 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.

  5. 2018

    1. 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.
    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
    3. 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.

  6. 2017

    1. 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.
    2. 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.
    3. A. Barth and A. Stein, “A study of elliptic partial differential equations with jump diffusion  coefficients,” 2017.
    4. 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
    5. M. Köppel et al., “Datasets and executables of data-driven uncertainty quantification benchmark in carbon dioxide storage.” Nov. 2017. doi: 10.5281/zenodo.933827.

  7. 2016

    1. A. Barth and A. Stein, “Approximation and simulation of infinite-dimensional Lévy processes,” 2016. [Online]. Available: http://arxiv.org/abs/1612.05541
    2. 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, vol. 163, Springer, Cham, 2016, pp. 209--227. doi: 10.1007/978-3-319-33507-0_8.
    3. 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.
    4. A. Barth and I. Kröker, “Finite volume methods for hyperbolic partial differential equations  with spatial noise,” in Springer Proceedings in Mathematics and Statistics, vol. submitted, Springer International Publishing, 2016.
    5. 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.
    6. 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.

  8. 2014

    1. 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.
    2. 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.

  9. 2013

    1. 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.
    2. 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.
    3. 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.

  10. 2012

    1. 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.
    2. 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.
    3. 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.

  11. 2011

    1. 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.
    2. 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.

  12. 2010

    1. 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

  13. 2009

    1. A. Barth, “Stochastic Partial Differential Equations: Approximations  and Applications,” University of Oslo, CMA, 2009. [Online]. Available: https://www.duo.uio.no/handle/10852/10669

  14. 2006

    1. A. Barth, “Distribution of the First Rendezvous Time of Two Geometric  Brownian Motions,” 2006.

Contact

This image shows Andrea Barth

Andrea Barth

Prof. Dr.

Head of Group

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