Göttinger Graduiertenschule für Neurowissenschaften, Biophysik und Molekulare Biowissenschaften

Prof. Dr. Thorsten Hohage

Professor, Institute for Numerical and Applied Mathematics

  • 1996-1998 research assistent at Institute of Industrial Mathematics, Johannes-Kepler University Linz, Austria
  • 1998-2000 research assistent at CRC F013 Numerical and Symbolic Scientific Computing, Linz, Austria
  • 2000-2002 posdoc at Zuse Institute Berlin with Peter Deuflhard
  • 2002-2007 junior professor at Institute of Numerical and Applied Mathematics, Georg-August Universität Göttingen
  • 2007-2009 associate (W2) professor at Georg-August Univerität Göttingen
  • Since 2009 full (W3) professor at Georg-August Universität Göttingen

Major Research Interests

  • inverse problems

  • inverse problems in partial differential equations, in particular inverse scattering problems

  • regularization theory for statistical inverse problems

  • variational regularization

  • efficient algorithms

  • application areas: phase retrieval problems in optics, Magnetic Resonance Imaging (MRI), helioseismology,

  • transparent boundary condition, resonances

  • spectrally convergent methods, in particular Hardy space infinite elements

  • numerical computation of resonances

  • Helmholtz, Maxwell, and elasticity equations

  • back-propagating modes

Homepage Department/Research Group


Selected Recent Publications

  • Thorsten Hohage, Frederic Weidling. 2017. Characterizations of variational source conditions, converse results, and maxisets of spectral regularization methods. SIAM J. Numer. Anal. 55(2): 598-620. doi:10.3934/ipi.2017010

  • Simon Maretzke, Thorsten Hohage. 2017. Stability estimates for linearized near-field phase retrieval in X-ray phase contrast imaging. SIAM J. Appl. Math. 77(2): 384-408. doi:10.1137/16M1086170

  • Helen Schomburg, Thorsten Hohage. 2017. Semi-Local Tractography Strategies Using Neighborhood Information. Medical Image Analysis 38: 165-183. doi:10.1016/j.media.2017.03.003

  • Thorsten Hohage, Frank Werner. 2016. Inverse Problems with Poisson Data: statistical regularization theory, applications and algorithms. Inverse Problems 32: 093001:56pp. doi:10.1088/0266-5611/32/9/093001

  • Simon Maretzke, Matthias Bartels, Martin Krenkel, Tim Salditt, Thorsten Hohage. 2016. Regularized Newton methods for X-ray phase contrast and general imaging problems. Optics Express 24(6): 6490-6506. doi:10.1364/OE.24.006490

  • M. Uecker, T. Hohage, K. T. Block, J. Frahm. 2008. Image Reconstruction by Regularized Nonlinear Inversion - Joint Estimation of Coil Sensitivities and Image Content. Magnetic Resonance in Medicine 60: 674-682. doi:10.1002/mrm.21691