Publications

List of publications of the Research Group

  1. 2021

    1. A. Beck, J. Dürrwächter, T. Kuhn, F. Meyer, C.-D. Munz, and C. Rohde, “Uncertainty Quantification in High Performance Computational Fluid Dynamics,” in High Performance Computing in Science and Engineering ’19, Cham, 2021, pp. 355--371.
    2. J. Dürrwächter, F. Meyer, T. Kuhn, A. Beck, C.-D. Munz, and C. Rohde, “A high-order stochastic Galerkin code for the compressible Euler and Navier-Stokes equations,” Computers & Fluids, p. 105039, 2021, doi: 10.1016/j.compfluid.2021.105039.

  2. 2019

    1. T. Kuhn, J. Dürrwächter, F. Meyer, A. Beck, C. Rohde, and C.-D. Munz, “Uncertainty quantification for direct aeroacoustic simulations of cavity flows,” J. Theor. Comput. Acoust., vol. 27, no. 1, 1850044, Art. no. 1, 1850044, 2019, doi: https://doi.org/10.1142/S2591728518500445.
    2. M. Köppel et al., “Comparison of data-driven uncertainty quantification methods for  a carbon dioxide storage benchmark scenario,” Comput. Geosci., vol. 2, no. 23, Art. no. 23, 2019, doi: https://doi.org/10.1007/s10596-018-9785-x.

  3. 2018

    1. 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

  4. 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. S. De Marchi, A. Idda, and G. Santin, “A Rescaled Method for RBF Approximation,” in Approximation Theory XV: San Antonio 2016, G. E. Fasshauer and L. L. Schumaker, Eds. Cham: Springer International Publishing, 2017, pp. 39--59. doi: 10.1007/978-3-319-59912-0_3.

  5. 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.
    7. K. Carlberg, L. Brencher, B. Haasdonk, and A. Barth, “Data-driven time parallelism via forecasting,” 2016.

  6. 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.

  7. 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.

  8. 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.
    4. A. Corli and C. Rohde, “Singular limits for a parabolic-elliptic regularization of scalar conservation laws,” J. Differential Equations, vol. 253, no. 5, Art. no. 5, 2012, doi: 10.1016/j.jde.2012.05.006.

  9. 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.

  10. 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

  11. 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

  12. 2006

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

  13. 2005

    1. P. Bastian et al., “Towards a Unified Framework for Scientific Computing,” in Domain Decomposition Methods in Science and Engineering, 2005, no. 40, pp. 167–174. doi: 10.1007/3-540-26825-1_13.
    2. A. Schmidt and K. G. Siebert, Design of Adaptive Finite Element Software. The Finite Element  Toolbox ALBERTA, vol. 42. Berlin: Springer, 2005. doi: 10.1007/b138692.

Contact

This image showsAndrea Barth
Prof. Dr.

Andrea Barth

Head of Group

To the top of the page