Timo Welti

ETH Zurich Seminar for Applied Mathematics Department of Mathematics HG G 54.1 Rämistrasse 101 8092 Zurich Switzerland
tel:+41 44 632 0392 mailto:timo.welti@sam.math.ethz.ch http://twelti.org

Born: October 1993 (age 25)

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Education

since 06/2015 PhD student at the Seminar for Applied Mathematics, ETH Zurich
05/2015 Master degree in Mathematics, ETH Zurich
09/2014 Bachelor degree in Mathematics, ETH Zurich
08/2013-01/2014 Term abroad at NUS (Singapore)
08/2010 Matura at the Mathematisch-Naturwissenschaftliches Gymnasium Rämibühl

Preprints and publications

  • Jentzen, A., Salimova, D., and Welti, T., A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients. [arXiv] (2018), 48 pages.

  • Jentzen, A., Salimova, D., and Welti, T., Strong convergence for explicit space–time discrete numerical approximation methods for stochastic Burgers equations. J. Math. Anal. Appl. 469 (2019), no. 2, 661-704. [arXiv]

  • Jacobe de Naurois, L., Jentzen, A., and Welti, T., Lower Bounds for Weak Approximation Errors for Spatial Spectral Galerkin Approximations of Stochastic Wave Equations. In: Stochastic Partial Differential Equations and Related Fields Springer International Publishing, Cham, 2018, 237-248. [arXiv]

  • Andersson, A., Jentzen, A., Kurniawan, R., and Welti, T., On the differentiability of solutions of stochastic evolution equations with respect to their initial values. Nonlinear Anal. 162 (2017), 128-161. [arXiv]

  • Cox, S., Hutzenthaler, M., Jentzen, A., van Neerven, J., and Welti, T., Convergence in Hölder norms with applications to Monte Carlo methods in infinite dimensions. [arXiv] (2016), 48 pages. To appear in IMA J. Num. Anal.

  • Jacobe de Naurois, L., Jentzen, A., and Welti, T., Weak convergence rates for spatial spectral Galerkin approximations of semilinear stochastic wave equations with multiplicative noise. [arXiv] (2015), 27 pages. Accepted in Appl. Math. Optim.