Skip to main content

Staff Dr. Tim Jahn

Contact Information

Institut für Numerische Simulation
Friedrich-Hirzebruch-Allee 7
53115 Bonn
Phone: +49 228 73--69813
Office: FHA7 3.005
E-Mail: ed tod nnob-inu tod sni ta nhaja tod b@foo tod de


Winter semester 2021/22

See teaching activities of the whole group.



  1. Regularizing linear inverse problems under unknown non-Gaussian white noise allowing repeated measurements. B. Harrach, T. Jahn, and R. Potthast. IMA Journal of Numerical Analysis, 01 2022. drab098. BibTeX DOI arXiv arxiv
  2. A probabilistic oracle inequality and quantification of uncertainty of a modified discrepancy principle for statistical inverse problems. T. Jahn. Electronic Transactions on Numerical Analysis, 57:35–56, 2022. BibTeX DOI arxiv
  3. Discretisation-adaptive regularisation of statistical inverse problems. T. Jahn. preprint, 2022. BibTeX arxiv
  4. Noise level free regularisation of general linear inverse problems under unconstrained white noise. T. Jahn. preprint, 2022. BibTeX arxiv
  5. Optimal convergence of the discrepancy principle for polynomially and exponentially ill-posed operators under white noise. T. Jahn. Numerical Functional Analysis and Optimization, 43(2):145–167, 2022. BibTeX DOI arXiv arxiv
  6. A modified discrepancy principle to attain optimal convergence rates under unknown noise. T. Jahn. Inverse Problems, 37(9):095008, 2021. BibTeX DOI arxiv
  7. Beyond the bakushinkii veto: regularising linear inverse problems without knowing the noise distribution. B. Harrach, T. Jahn, and R. Potthast. Numerische Mathematik, 145(3):581–603, 2020. BibTeX DOI arxiv
  8. On the discrepancy principle for stochastic gradient descent. T. Jahn and B. Jin. Inverse Problems, 36(9):095009, 2020. BibTeX DOI arxiv
  9. The sensorimotor loop as a dynamical system: how regular motion primitives may emerge from self-organized limit cycles. B. Sándor, T. Jahn, L. Martin, and C. Gros. Frontiers in Robotics and AI, 2:31, 2015. BibTeX DOI


  1. Regularising linear inverse problems under unknown non-Gaussian noise. T. N. Jahn. PhD thesis, Institute for Mathematics, Johann Wolfgang Goethe-Universität Frankfurt am Main, 2020. BibTeX PDF
  2. The high temperature regime of a multi-species mean field spin glass. T. Jahn. Master's thesis, Insititute for Mathematics, Johann Wolfgang Goethe-Universität Frankfurt am Main, 2016. BibTeX PDF
  3. Limit cycles of neuron controlled robots in simulated physical environment. T. Jahn. Bachelor's Thesis, Institute for Theoretical Physics, Johann Wolfgang Goethe-Universität Frankfurt am Main, 2015. BibTeX PDF