Diyora Salimova
University of Freiburg Address:
Junior Prof. Dr. Diyora Salimova
Department for Applied Mathematics
Mathematical Institute
University of Freiburg
Hermann-Herder-Str. 10, Room 208
79104 Freiburg im Breisgau
Germany
01/2020: Postdoc at ETH Zurich, D-MATH, Seminar for Applied Mathematics
Education
09/2016-12/2019: Doctor of Sciences of ETH Zurich, Switzerland. PhD supervisor: Prof. Dr. Arnulf Jentzen
09/2013-10/2015: Master of Science in Applied Mathematics, ETH Zurich, Switzerland
09/2011-06/2013: Bachelor of Science in Mathematics, Jacobs University Bremen, Germany
09/2009-06/2011: Completed two years of study in undergraduate Mathematics, Samarkand State University, Uzbekistan
Preprints
(authors listed in alphabetical order, except of *)
*Settelmeier, J., Goetze, S., Boshart, J., Fu, J., Steiner, S. N., Gesell, M., Schüffler, P. J., Salimova, D., Pedrioli, P. G. A., and Wollscheid, B.,
MultiOmicsAgent: Guided extreme gradient-boosted decision trees-based approaches for biomarker-candidate discovery in multi-omics data.[bioRxiv] (2024).
Bercher, A., Gonon, L., Jentzen, A., and Salimova, D.,
Weak error analysis for stochastic gradient descent optimization algorithms.[arXiv] (2020), 123 pages.
Beccari, M., Hutzenthaler, M., Jentzen, A., Kurniawan, R., Lindner, F., and Salimova, D.,
Strong and weak divergence of exponential and linear-implicit Euler approximations for stochastic partial differential equations with superlinearly growing nonlinearities.[arXiv] (2019), 65 pages.
Jentzen, A., Mazzonetto, S., and Salimova, D.,
Existence and uniqueness properties for solutions of a class of Banach space valued evolution equations.[arXiv] (2018), 28 pages.
Published papers
(authors listed in alphabetical order)
Hornung, F., Jentzen, A., and Salimova, D.,
Space-time deep neural network approximations for high-dimensional partial differential equations.
To appear in J. Comput. Math.[arXiv]
Hermann, T., Niedziela, D., Salimova, D., and Schweiger, T.,
Predicting the fiber orientation of injected molded components and the geometry influence with neural networks,Journal of Composite Materials, 2024;58(15):1801–1811
Baggenstos, J., and Salimova, D.,
Approximation properties of Residual Neural Networks for Kolmogorov PDEs.Discrete Contin. Dyn. Syst. Ser. B. 28 (2023), no. 5, 3193-3215.
[arXiv]
Grohs, P., Jentzen, A., and Salimova, D.,
Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms.Partial Differ. Equ. Appl.3 (2022), no. 4, Paper No. 45.
[arXiv]
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.Comm. Math. Sci.19 (2021), no. 5, 1167-1205.
[arXiv].
Mazzonetto, S., and Salimova, D.,
Existence, uniqueness, and numerical approximations for stochastic Burgers equations.Stoch. Anal. Appl.38 (2020), no. 4, 623-646.
[arXiv].
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].
Hutzenthaler, M., Jentzen, A., and Salimova, D.,
Strong convergence of full-discrete nonlinearity-truncated accelerated exponential Euler-type approximations for stochastic Kuramoto-Sivashinsky equations.Comm. Math. Sci.16 (2018), no. 6, 1489-1529.
[arXiv].
Gerencsér, M., Jentzen, A., and Salimova, D.,
On stochastic differential equations with arbitrarily slow convergence rates for strong approximation in two space dimensions.Proc. Roy. Soc. London A473 (2017).
[arXiv].
Ibragimov, Z. and Salimova, D.,
On an inequality in l_p(C) involving Basel problem.Elem. Math.70 (2015), 79-81.
05/2024: Probability and Statistics Seminar of IECL, Nancy, France
05/2024: Mathematical Colloquium, Karlsruhe Institute of Technology, Germany
03/2024: One World Stochastic Numerics and Inverse Problems (OWSNIP) seminar, online
10/2023: Minisymposium: Mathematical Data Science and Optimization, Univeristy of Heidelberg, Germany
12/2022: Oberseminar Stochastik, University of Freiburg, Germany
08/2022: 2022 Workshops on Theory of Machine Learning, Chalmers University, Gothenburg, Sweden
07/2022: Continuous Time Methods for Machine Learning workshop, ICML 2022, Baltimore, Maryland USA
02/2022: Oberseminar Angewandte Mathematik, University of Freiburg, Germany
09/2021: Section
''Stochastics and Financial Mathematics" at the German Mathematical Society and the Austrian Mathematical Society
(DMV ÖMG) Annual Conference 2021
08/2021: Special session
''Stochastic Computation and Complexity"
at the
13th International Conference on Monte Carlo Methods and Applications (MCM)
02/2021: Applied Mathematics Seminar, KU Eichstätt-Ingolstadt
11/2020: Oxford Stochastic Analysis and Mathematical Finance Seminar
04/2019: Numerical Analysis Seminar, University of Geneva, Switzerland
03/2019: Research Seminar in Mathematics for Economics and Business, WU Vienna, Austria
03/2019: Seminar Talk, EPFL Lausanne, Switzerland
09/2018: Austrian Stochastics Days 2018, Vienna, Austria
07/2018: Special session
''Numerical approximation of SDEs under non-standard assumptions"
at the
13th International Conference in Monte Carlo & Quasi-Monte Carlo in Scientific Computing (MCQMC), Rennes, France
07/2017: Special session
Stochastic Computation workshop at the Foundation of Computational Mathematics (FoCM) conference 2017, Barcelona, Spain