Prof. Dr. Simon Weißmann

Prof. Dr. Simon Weißmann

Assistant Professor of applied stochastics
University of Mannheim
Mathematical Institute
B6, 26 – Room B 3.05
68159 Mannheim

Short CV

Research Interests

  • (Bayesian) Inverse Problems
  • (Stochastic) Optimization
  • (Ensemble) Kalman Filtering
  • Monte Carlo methods
  • Rare event simulation
  • Application in Machine Learning 



University of Mannheim

HWS 2023

Mathematical Finance (BSc)
FSS 2023

Optimization in Machine Learning (BSc/MSc)


University of Mannheim
HWS 2023

Advanced Seminar on Mathematical methods in Artificial Intelligence (BSc/MSc)

Heidelberg University

WiSe 2021

Simulation of Stochastic Systems (BSc/MSc)


Preprints and submitted manuscripts


Adaptive multilevel subset simulation with selective refinement

D. Elfverson, R. Scheichl, S. Weissmann and F. A. DiazDelaO

Preprint: arXiv:2208.05392


One-shot learning of surrogates in PDE-constrained Optimization under Uncertainty

P. Guth, C. Schillings and S. Weissmann

Preprint: arXiv:2112.11126

Articles in journals and refereed proceedings volumes


Continuous time limit of the stochastic ensemble Kalman inversion: Strong convergence analysis

D. Blömker, C. Schillings, P. Wacker and S. Weissmann

SIAM Journal on Numerical Analysis, Volume 60, Issue 6, doi: 10.1137/21M1437561

Gradient flow structure and convergence analysis of the ensemble Kalman inversion for nonlinear forward models

S. Weissmann

Inverse Problems, Volume 38, Number 10, doi: 10.1088/1361-6420/ac8bed

Multilevel optimization for inverse problems

S. Weissmann, A. Wilson and J. Zech

Proceedings of Thirty Fifth Conference on Learning Theory (COLT 2022), PMLR 178:5489-5524

Adaptive Tikhonov regularization for stochastic ensemble Kalman inversion

S. Weissmann, N. Chada, C. Schillings and X. Tong

Inverse Problems, Volume 38, Number 4, doi: 10.1088/1361-6420/ac5729

Consistency analysis of bilevel data-driven regularization in inverse problems

N. Chada, C. Schillings, X. Tong and S. Weissmann

Communications in Mathematical Sciences, Volume 20, Number 1, doi: 10.4310/CMS.2022.v20.n1.a4


Ensemble Kalman filter for neural network based one-shot inversion

P. Guth, C. Schillings and S. Weissmann

In: Optimization and Control for Partial Differential Equations, De Gruyter, doi: 10.1515/9783110695984-014

Fokker–Planck particle systems for Bayesian inference: computational approaches

S. Reich and S. Weissmann

SIAM/ASA Journal on Uncertainty Quantification, Volume 9, Issue 2, doi: 10.1137/19M1303162

Well posedness and convergence analysis of the ensemble Kalman Inversion

D. Blömker, C. Schillings, P. Wacker and S. Weissmann

Inverse Problems, Volume 35, Number 8, doi: 10.1088/1361-6420/ab149c

On the incorporation of box-constraints for ensemble Kalman Inversion

N. Chada, C. Schillings and S. Weissmann

Foundations of Data Science, Volume 1, Issue 4, doi: 10.3934/fods.2019018



    Particle based sampling and optimization methods for inverse problems

    S. Weissmann

    Dissertation, University of Mannheim, link


    Perpetual Integrals for stochastic processes

    S. Weissmann

    Master Thesis, University of Mannheim


    Renewal processes – Alternation, excesslife and agedistribution

    S. Weissmann

    Bachelor Thesis, University of Mannheim