M.Sc. David John

Phone: +49 711 811-22400
Room: --
david.john <_at_> de.bosch.com


Office address/Post address

Engineering Mathematics and Computing Lab (EMCL)
Interdisciplinary Center for Scientific Computing (IWR)
Im Neuenheimer Feld 205
69120 Heidelberg (Germany)

Short Biography

In December 2016 I finished my studies of technical mathematics at Karlsruhe Institute of Technology (KIT) with mechanical engineering and applied computer science as minor subjects. My master thesis was about "Uncertainty Quantification for hydraulic sytems", with focus on the anisotropic Smolyak sparse pseudo-spectral projection method.

In January 2017 I entered the PhD program of Robert Bosch GmbH and joined the Engineering Mathematics and Computing Lab (EMCL) directed by Prof. Dr. Vincent Heuveline as an external collaborator. My research topic is in the field of Uncertainty Quantification.

Research Interests

Uncertainty Quantification with focus on

  • Inverse problems
  • Parameter identification
  • Model inadequacy
  • Bayesian inference
  • Sensitivity Analysis
  • Uncertainty propagation
  • Surrogate models


Participations, conference contributions and talks

  • D. John, M. Schick, V. Heuveline: Learning model discrepancy of an electric motor with Bayesian inference. 38th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, The Alan Turing Institute, British Library, London, UK, 2.-6. July 2018
  • D. John, M. Schick, V. Heuveline: Bayesian inference for estimating model discrepancy of an electric drive model. SIAM Conference on Uncertainty Quantification (UQ18), Los Angeles, USA, 16.-19. April 2018
  • D. John, M. Schick, V. Heuveline: Bayesian inference for estimating model discrepancy of an electric drive model. 89th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Munich, Germany, 19.-23. March 2018
  • Participation in: 4th GAMM Junior's and 1st GRK2075 Summer School 2017Bayesian Inference: Probabilistic way of learning from data. Braunschweig, Germany, 10.-14. July 2017
  • Participation in: TUM-IAS Focal Period 2017 International Symposium: Machine Learning Challenges in Complex Multiscale Physical Systems. Munich, Germany, 9.-12. January 2017