Chair of Computational Science
Clausiusstrasse 33
ETH-Zentrum, CLT C 14
CH-8092 Zürich

Contact Information


Pascal Weber

PhD Student

Research and Interests

  • Reinforcement- and Deep Learning
  • Self propelled anguilliform swimmers
  • Optimization and Bayesian Inference
  • High Performance Computing


  • Master of Science in Physics, ETH Zürich, 2018
  • Bachelor of Science in Interdisciplinary Science, ETH Zürich, 2016

Teaching Assistance

  • Models, Algorithms and Data (MAD): Introduction to Computing – Spring 2020
  • Models, Algorithms and Data (MAD): Introduction to Computing – Spring 2019



  • P. Weber, G. Arampatzis, G. Novati, S. Verma, C. Papadimitriou, and P. Koumoutsakos, “Optimal flow sensing for schooling swimmers,” Biomimetics, vol. 5, iss. 1, 2020.
    [BibTeX] [Abstract] [PDF] [DOI]

    Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other fish. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with numerical simulations of the two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and the number of the leading swimmers using surface only information.

    article-number = {10},
    author = {Weber, Pascal and Arampatzis, Georgios and Novati, Guido and Verma, Siddhartha and Papadimitriou, Costas and Koumoutsakos, Petros},
    doi = {10.3390/biomimetics5010010},
    issn = {2313-7673},
    journal = {Biomimetics},
    number = {1},
    title = {Optimal Flow Sensing for Schooling Swimmers},
    url = {},
    volume = {5},
    year = {2020}


  • G. Arampatzis, D. Wälchli, P. Weber, H. Rästas, and P. Koumoutsakos, “(μ,Λ)-ccma-es for constrained optimization with an application in pharmacodynamics,” in Proceedings of the platform for advanced scientific computing conference – PASC ’19, 2019.
    [BibTeX] [PDF] [DOI]
    author = {Arampatzis, Georgios and W\"{a}lchli, Daniel and Weber, Pascal and R\"{a}stas, Henri and Koumoutsakos, Petros},
    booktitle = {Proceedings of the Platform for Advanced Scientific Computing Conference - {PASC} {\textquotesingle}19},
    title = {(\Μ,{\Lambda})-CCMA-ES for Constrained Optimization with an Application in Pharmacodynamics},
    year = {2019},
    publisher = {{ACM} Press},
    doi = {10.1145/3324989.3325725},
    keywords = {Stochastic optimization, constraint handling, covariance matrix adaptation, evolution strategy, pharmacodynamics, viability evolution},
    url = {},