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 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

Software Contributions

  • Korali, a high-performance framework for Bayesian UQ, optimization, and reinforcement learning.
  • CubismUP, a high-fidelity Navier-Stokes solver for incompressible flows around obstacles with distributed block-structured adaptively refined grids.

Teaching Assistance

  • Introduction to Fluid Mechanics and Transport Processes (ES-123) – Harvard University – Spring 2022
  • High Performance Computing for Science and Engineering (HPCSE) I – Autumn 2021
  • Models, Algorithms and Data (MAD): Introduction to Computing – Spring 2019-2021


Google scholar


  • M. Chatzimanolakis, P. Weber, and P. Koumoutsakos, “Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to Re=100’000,” Journal of fluid mechanics, vol. 953, 2022.
    [BibTeX] [PDF] [DOI]
    author = {Michail Chatzimanolakis and Pascal Weber and Petros Koumoutsakos},
    doi = {10.1017/jfm.2022.988},
    journal = {Journal of Fluid Mechanics},
    month = {dec},
    publisher = {Cambridge University Press ({CUP})},
    title = {Vortex separation cascades in simulations of the planar flow past an impulsively started cylinder up to {R}e=100'000},
    url = {},
    volume = {953},
    year = {2022}


  • I. Mandralis, P. Weber, G. Novati, and P. Koumoutsakos, “Learning swimming escape patterns for larval fish under energy constraints,” Phys. Rev. Fluids, vol. 6, p. 93101, 2021.
    [BibTeX] [PDF] [DOI]
    author = {Mandralis, Ioannis and Weber, Pascal and Novati, Guido and Koumoutsakos, Petros},
    doi = {10.1103/PhysRevFluids.6.093101},
    issue = {9},
    journal = {{Phys. Rev. Fluids}},
    month = {Sep},
    numpages = {15},
    pages = {093101},
    publisher = {American Physical Society},
    title = {Learning swimming escape patterns for larval fish under energy constraints},
    url = {},
    volume = {6},
    year = {2021}


  • M. Chatzimanolakis, P. Weber, G. Arampatzis, D. Wälchli, I. Kičić, P. Karnakov, C. Papadimitriou, and P. Koumoutsakos, “Optimal allocation of limited test resources for the quantification of COVID-19 infections,” Swiss Med. Wkly., 2020.
    [BibTeX] [PDF] [DOI]
    author = {Michail Chatzimanolakis and Pascal Weber and Georgios Arampatzis and Daniel W{\"a}lchli and Ivica Ki\v{c}i\'{c} and Petr Karnakov and Costas Papadimitriou and Petros Koumoutsakos},
    doi = {10.4414/smw.2020.20445},
    journal = {{Swiss Med. Wkly.}},
    month = {dec},
    publisher = {{EMH} Swiss Medical Publishers, Ltd.},
    title = {Optimal allocation of limited test resources for the quantification of {COVID}-19 infections},
    url = {},
    year = {2020}

  • 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},
    doi = {10.1145/3324989.3325725},
    keywords = {Stochastic optimization, constraint handling, covariance matrix adaptation, evolution strategy, pharmacodynamics, viability evolution},
    publisher = {{ACM} Press},
    title = {(\Μ,{\Lambda})-CCMA-ES for Constrained Optimization with an Application in Pharmacodynamics},
    url = {},
    year = {2019}