Address

Chair of Computational Science
Clausiusstrasse 33
ETH-Zentrum, CLT E 12
CH-8092 Zürich

Contact Information

Email:

Georgios Arampatzis

Post-doctoral Fellow

Research and Interests

  • Bayesian Uncertainty Quantification
  • Hierarchical Bayesian Statistics
  • Optimal Sensor Placement

Education

  • Ph.D. Applied Mathematics, University of Crete, Greece. 2014
  • M.S. Applied Mathematics, University of Crete, Greece. 2011

2020

  • P. Karnakov, S. Litvinov, and P. Koumoutsakos, “A hybrid particle volume-of-fluid method for curvature estimation in multiphase flows,” Int. J. Multiphas. Flow, vol. 125, p. 103209, 2020.
    [BibTeX] [PDF] [DOI]
    @article{karnakov2020a,
    author = {Petr Karnakov and Sergey Litvinov and Petros Koumoutsakos},
    doi = {10.1016/j.ijmultiphaseflow.2020.103209},
    journal = {{Int. J. Multiphas. Flow}},
    month = {apr},
    pages = {103209},
    publisher = {Elsevier {BV}},
    title = {A hybrid particle volume-of-fluid method for curvature estimation in multiphase flows},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/karnakov2020a.pdf},
    volume = {125},
    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{weber2020a,
    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 = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/weber2020a.pdf},
    volume = {5},
    year = {2020}
    }

  • D. Wälchli, S. M. Martin, A. Economides, L. Amoudruz, G. Arampatzis, X. Bian, and P. Koumoutsakos, “Load balancing in large scale bayesian inference,” in Proceedings of the platform for advanced scientific computing conference – PASC ’20, 2020.
    [BibTeX] [PDF] [DOI]
    @InProceedings{walchli2020a,
    author = {Daniel W\"{a}lchli and Sergio M. Martin and Athena Economides and Lucas Amoudruz and George Arampatzis and Xin Bian and Petros Koumoutsakos},
    booktitle = {Proceedings of the Platform for Advanced Scientific Computing Conference – {PASC} {\textquotesingle}20},
    title = {Load Balancing in Large Scale Bayesian Inference},
    year = {2020},
    month = {jun},
    publisher = {{ACM}},
    doi = {10.1145/3394277.3401849},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/walchli2020a.pdf},
    }

  • F. Cailliez, P. Pernot, F. Rizzi, R. Jones, O. Knio, G. Arampatzis, and P. Koumoutsakos, “Bayesian calibration of force fields for molecular simulations,” in Uncertainty quantification in multiscale materials modeling, Y. Wang and D. McDowell, Eds., Elsevier, 2020, pp. 169-277.
    [BibTeX] [PDF] [DOI]
    @incollection{fcailliez2020a,
    author = {F. Cailliez and P. Pernot and F. Rizzi and R. Jones and O. Knio and G. Arampatzis and P. Koumoutsakos},
    booktitle = {Uncertainty Quantification in Multiscale Materials Modeling},
    chapter = {6},
    doi = {10.1016/B978-0-08-102941-1.00006-7},
    editor = {Wang, Yan and McDowell, David},
    pages = {169-277},
    publisher = {Elsevier},
    title = {Bayesian calibration of force fields for molecular simulations},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/fcailliez2020a.pdf},
    year = {2020}
    }

2019

  • W. Byeon, M. Domínguez-Rodrigo, G. Arampatzis, E. Baquedano, J. Yravedra, M. A. Maté-González, and P. Koumoutsakos, “Automated identification and deep classification of cut marks on bones and its paleoanthropological implications,” J. Comput. Sci., vol. 32, pp. 36-43, 2019.
    [BibTeX] [Abstract] [PDF] [DOI]

    The identification of cut marks and other bone surface modifications (BSM) provides evidence for the emergence of meat-eating in human evolution. This most crucial part of taphonomic analysis of the archaeological human record has been controversial due to highly subjective interpretations of BSM. Here, we use a sample of 79 trampling and cut marks to compare the accuracy in mark identification on bones by human experts and computer trained algorithms. We demonstrate that deep convolutional neural networks (DCNN) and support vector machines (SVM) can recognize marks with accuracy that far exceeds that of human experts. Automated recognition and analysis of BSM using DCNN can achieve an accuracy of 91% of correct identification of cut and trampling marks versus a much lower accuracy rate (63%) obtained by trained human experts. This success underscores the capability of machine learning algorithms to help resolve controversies in taphonomic research and, more specifically, in the study of bone surface modifications. We envision that the proposed methods can help resolve on-going controversies on the earliest human meat-eating behaviors in Africa and other issues such as the earliest occupation of America.

    @article{byeon2019a,
    author = {Wonmin Byeon and Manuel Dom{\'{\i}}nguez-Rodrigo and Georgios Arampatzis and Enrique Baquedano and Jos{\'{e}} Yravedra and Miguel Angel Mat{\'{e}}-Gonz{\'{a}}lez and Petros Koumoutsakos},
    doi = {10.1016/j.jocs.2019.02.005},
    issn = {1877-7503},
    journal = {{J. Comput. Sci.}},
    pages = {36 - 43},
    title = {Automated identification and deep classification of cut marks on bones and its paleoanthropological implications},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/byeon2019a.pdf},
    volume = {32},
    year = {2019}
    }

  • S. Verma, C. Papadimitriou, N. Luethen, G. Arampatzis, and P. Koumoutsakos, “Optimal sensor placement for artificial swimmers,” J. Fluid Mech., vol. 884, 2019.
    [BibTeX] [PDF] [DOI]
    @article{verma2019a,
    author = {Siddhartha Verma and Costas Papadimitriou and Nora Luethen and Georgios Arampatzis and Petros Koumoutsakos},
    doi = {10.1017/jfm.2019.940},
    journal = {{J. Fluid Mech.}},
    month = {dec},
    publisher = {Cambridge University Press ({CUP})},
    title = {Optimal sensor placement for artificial swimmers},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/verma2019a.pdf},
    volume = {884},
    year = {2019}
    }

  • J. Zavadlav, G. Arampatzis, and P. Koumoutsakos, “Bayesian selection for coarse-grained models of liquid water,” Sci. Rep.-UK, vol. 9, iss. 1, 2019.
    [BibTeX] [PDF] [DOI]
    @article{zavadlav2019a,
    author = {Julija Zavadlav and Georgios Arampatzis and Petros Koumoutsakos},
    doi = {10.1038/s41598-018-37471-0},
    journal = {{Sci. Rep.-UK}},
    month = {jan},
    number = {1},
    publisher = {Springer Nature},
    title = {Bayesian selection for coarse-grained models of liquid water},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/zavadlav2019a.pdf},
    volume = {9},
    year = {2019}
    }

  • 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]
    @InProceedings{arampatzis2019a,
    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 = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/arampatzis2019a.pdf},
    }

2018

  • G. Arampatzis, D. Wälchli, P. Angelikopoulos, S. Wu, P. Hadjidoukas, and P. Koumoutsakos, “Langevin diffusion for population based sampling with an application in bayesian inference for pharmacodynamics,” SIAM J. Sci. Comput., vol. 40, iss. 3, p. B788–B811, 2018.
    [BibTeX] [PDF] [DOI]
    @article{arampatzis2018a,
    author = {Georgios Arampatzis and Daniel W\"alchli and Panagiotis Angelikopoulos and Stephen Wu and Panagiotis Hadjidoukas and Petros Koumoutsakos},
    doi = {10.1137/16m1107401},
    journal = {{SIAM J. Sci. Comput.}},
    month = {jan},
    number = {3},
    pages = {B788--B811},
    publisher = {Society for Industrial {\&} Applied Mathematics ({SIAM})},
    title = {Langevin Diffusion for Population Based Sampling with an Application in Bayesian Inference for Pharmacodynamics},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/arampatzis2018a.pdf},
    volume = {40},
    year = {2018}
    }

  • J. Lipková, G. Arampatzis, P. Chatelain, B. Menze, and P. Koumoutsakos, “S-leaping: an adaptive, accelerated stochastic simulation algorithm, bridging τ-leaping and r-leaping,” B. Math. Biol., 2018.
    [BibTeX] [PDF] [DOI]
    @article{lipkova2018a,
    author = {Jana Lipkov{\'{a}} and Georgios Arampatzis and Philippe Chatelain and Bjoern Menze and Petros Koumoutsakos},
    doi = {10.1007/s11538-018-0464-9},
    journal = {{B. Math. Biol.}},
    month = {jul},
    publisher = {Springer Nature},
    title = {S-Leaping: An Adaptive, Accelerated Stochastic Simulation Algorithm, Bridging \tau-Leaping and R-Leaping},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/lipkova2018a.pdf},
    year = {2018}
    }

2017

  • L. Kulakova, G. Arampatzis, P. Angelikopoulos, P. Hadjidoukas, C. Papadimitriou, and P. Koumoutsakos, “Data driven inference for the repulsive exponent of the Lennard-Jones potential in molecular dynamics simulations,” Sci. Rep.-UK, vol. 7, iss. 1, p. 16576, 2017.
    [BibTeX] [Abstract] [PDF] [DOI]

    The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD) simulations and among the most widely used computational kernels in science. The LJ potential models atomistic attraction and repulsion with century old prescribed parameters (q=6, p=12 respectively), originally related by a factor of two for simplicity of calculations. We propose the inference of the repulsion exponent through Hierarchical Bayesian uncertainty quantification We use experimental data of the radial distribution function and dimer interaction energies from quantum mechanics simulations. We find that the repulsion exponent p\approx6.5 provides an excellent fit for the experimental data of liquid argon, for a range of thermodynamic conditions, as well as for saturated argon vapour. Calibration using the quantum simulation data did not provide a good fit in these cases. However, values p\approx12.7 obtained by dimer quantum simulations are preferred for the argon gas while lower values are promoted by experimental data. These results show that the proposed LJ 6-p potential applies to a wider range of thermodynamic conditions, than the classical LJ 6-12 potential. We suggest that calibration of the repulsive exponent in the LJ potential widens the range of applicability and accuracy of MD simulations.

    @article{kulakova2017a,
    author = {Kulakova, Lina and Arampatzis, Georgios and Angelikopoulos, Panagiotis and Hadjidoukas, Panagiotis and Papadimitriou, Costas and Koumoutsakos, Petros},
    doi = {10.1038/s41598-017-16314-4},
    issn = {2045-2322},
    journal = {{Sci. Rep.-UK}},
    number = {1},
    pages = {16576},
    title = {Data driven inference for the repulsive exponent of the {L}ennard-{J}ones potential in molecular dynamics simulations},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/kulakova2017a.pdf},
    volume = {7},
    year = {2017}
    }

  • B. Mosimann, G. Arampatzis, S. Amylidi-Mohr, A. Bessire, M. Spinelli, P. Koumoutsakos, D. Surbek, and L. Raio, “Reference ranges for fetal atrioventricular and ventriculoatrial time intervals and their ratios during normal pregnancy,” Fetal Diagn. Ther., 2017.
    [BibTeX] [PDF] [DOI]
    @article{mosimann2017a,
    author = {Beatrice Mosimann and Georgios Arampatzis and Sofia Amylidi-Mohr and Anice Bessire and Marialuigia Spinelli and Petros Koumoutsakos and Daniel Surbek and Luigi Raio},
    doi = {10.1159/000481349},
    journal = {{Fetal Diagn. Ther.}},
    month = {oct},
    publisher = {S. Karger {AG}},
    title = {Reference Ranges for Fetal Atrioventricular and Ventriculoatrial Time Intervals and Their Ratios during Normal Pregnancy},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/mosimann2017a.pdf},
    year = {2017}
    }