Machine Learning Network at NCBJ

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  • Agnieszka Pollo
    Department/group
    • DBP/BP4
    Research focus

    • large scale structure of the Universe, galaxy evolution, astrostatistics

    ML expertise

    • Various approaches; supervised/unsupervised

    Other

    • whatever enjoyable is good

      1. Solarz, A., Pollo, A., Takeuchi, T. T. et al., Star-galaxy separation in the AKARI NEP deep field, Astronomy & Astrophysics, Volume 541, id.A50, 8 pp, 2012, https://www.aanda.org/articles/aa/pdf/2012/05/aa18108-11.pdf

      2. Małek, K., Solarz, A., Pollo, A. et al., “The VIMOS Public Extragalactic Redshift Survey (VIPERS). A support vector machine classification of galaxies, stars, and AGNs”, Astronomy & Astrophysics, Volume 557, id.A16, 16 pp., 2013, https://www.aanda.org/articles/aa/full_html/2013/09/aa21447-13/aa21447-13.html

      3. Kurcz, A.; Bilicki, M.; Solarz, A.; Krupa, M.; Pollo, A.; Małek, K., Towards automatic classification of all WISE sources, Astronomy & Astrophysics, Volume 592, id.A25, 18 pp., 2016, https://www.aanda.org/articles/aa/pdf/2016/08/aa28142-16.pdf

      4. Krakowski, T.; Małek, K.; Bilicki, M.; Pollo, A.; Kurcz, A.; Krupa, M., “Machine-learning identification of galaxies in the WISE × SuperCOSMOS all-sky catalogue”,  Astronomy & Astrophysics, Volume 596, id.A39, 11 pp., 2016, https://ui.adsabs.harvard.edu/link_gateway/2016A%26A...596A..39K/PUB_PDF

      5. Solarz, A.; Bilicki, M.; Gromadzki, M.; Pollo, A.; Durkalec, A.; Wypych, M., Automated novelty detection in the WISE survey with one-class support vector machines, Astronomy & Astrophysics, Volume 606, id.A39, 13 pp., 2017, https://www.aanda.org/articles/aa/pdf/2017/10/aa30968-17.pdf

      6. Siudek, M.; Małek, K.; Pollo, A.; Krakowski, T.; Iovino, A. et al., “The VIMOS Public Extragalactic Redshift Survey (VIPERS). The complexity of galaxy populations at 0.4 < z < 1.3 revealed with unsupervised machine-learning algorithms”, Astronomy & Astrophysics, Volume 617, id.A70, 25 pp., 2018, https://ui.adsabs.harvard.edu/link_gateway/2018A%26A...617A..70S/PUB_PDF

      7. Nakoneczny, S.; Bilicki, M.; Solarz, A.; Pollo, A.; Maddox, N.; Spiniello, C.; Brescia, M.; Napolitano, N. R., Catalog of quasars from the Kilo-Degree Survey Data Release 3, Astronomy & Astrophysics, Volume 624, id.A13, 15 pp., 2019, https://www.aanda.org/articles/aa/pdf/2019/04/aa34794-18.pdf

      8. Poliszczuk, A.; Solarz, A.; Pollo, A.; Bilicki, M. et al., Active galactic nucleus selection in the AKARI NEP-Deep field with the fuzzy support vector machine algorithm, Publications of the Astronomical Society of Japan, Volume 71, Issue 3, id.65, https://arxiv.org/pdf/1902.04922.pdf

      9. Turner, S.; Siudek, M.; Salim, S.; Baldry, I. K.; Pollo, A.; Longmore, S. N.; Małek, K. et al.; Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning, Monthly Notices of the Royal Astronomical Society, 2021 in press, https://arxiv.org/pdf/2102.05056.pdf

      10. Nakoneczny, S. J.; Bilicki, M.; Pollo, A.; Asgari, M., et al., Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4, Astronomy & Astrophysics, , in press, 2021,  https://arxiv.org/pdf/2010.13857.pdf

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  • Fabrizio Rovaris
    Department/group
    • Nomaten/
    Research focus

    • 3D Discrete Dislocation Dynamics. Finite Element. Continuum numerical modeling of materials.

    ML expertise

    • Neural Networks, Sobolev Training, Active learning.

      1. D. Lanzoni, F. Rovaris and F. Montalenti, “Machine learning Dislocation Interactions: Reaching the Macroscopic Scale” in preparation (2021)
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  • Javier Dominguez
    Department/group
    • Nomaten/Complexity in Functional Materials
    Research focus

    • Numerical modeling of 2D materials catalysis, hydrogen generation by methane, irradiation in hydrogenated W samples.

    ML expertise

    • Quantum Espresso, VASP, SCC-DFTB, LAMMPS

    •  

       

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  • Kamran Karimi
    Department/group
    • Nomaten/
    Research focus

    • Statistical and Computational Physics, Plastic flow, Brittle Fracture, Rheology of Yield Stress Fluids, Statistical Seismology, Stochastic Modeling of Earthquakes

    ML expertise

    • computational/statistical libraries in Python

      1. Scikit-learn for linear (multivariate) regression, classification, and cluster analysis

      2. Pandas for data management, 

      3. Numpy/Scipy for linear algebra and optimization problems

    •  
      1. Karimi, Kamran, David Amitrano, and Jérôme Weiss. “From plastic flow to brittle fracture: Role of microscopic friction in amorphous solids". Phys. Rev. E, 100:012908, Jul 2019
      2. Karimi, Kamran and Jean-Louis Barrat. “Correlation and shear bands in a plastically deformed granular medium”. Scientific reports, 8(1):4021, 2018
      3. Kamran Karimi, Ezequiel E. Ferrero, and Jean-Louis Barrat. “Inertia and universality of avalanche statistics: The case of slowly deformed amorphous solids”. Physical Review E, 95(1):013003, 2017
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  • Katarzyna Małek
    Department/group
    • DBP/BP4
    Research focus

    • galaxy evolution, dust attenuation 

    ML expertise

    • SVM, FEM

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  • Mikko Alava
    Department/group
    • NOMATEN/Materials Complexity
    Research focus

    • Modelling materials, understanding experimental data

    ML expertise

    • Various approaches (SVD, CNN, Random Forests etc.)

    Other

    • enjoys drinking random things at random hours

      1. Machine learning plastic deformation of crystals, H Salmenjoki, MJ Alava, L Laurson, Nature communications 9 (1), 1-7 (2018).
      2. Machine learning and predicting the time-dependent dynamics of local yielding in dry foams, L Viitanen, JR Mac Intyre, J Koivisto, A Puisto, M Alava,, Physical Review Research 2 (2), 023338 (2020)
      3. Probing the transition from dislocation jamming to pinning by machine learning, H Salmenjoki, L Laurson, MJ Alava, Materials Theory 4 (1), 1-16 (2020)
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  • Silvia Bonfanti
    Department/group
    • NOMATEN/Materials Complexity
    Personal webpage

    • silviabonfantiphysics.com

    Research focus

    • Amorphous solids, Mechanical Metamaterials, Biophysics.

    ML expertise

    • CNN, SVM.

      1. Font-Clos, F., Zanchi, M., Hiemer, S., Bonfanti, S., Guerra, R., Zaiser, M., & Zapperi, S. (2022). Predicting the failure of two-dimensional silica glasses. arXiv preprint arXiv:2201.09723,
      2. Bonfanti, S., Guerra, R., Font-Clos, F., Rayneau-Kirkhope, D., & Zapperi, S. (2020). Automatic design of mechanical metamaterial actuators. Nature communications, 11(1), 1-10.
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  • Stefanos Papanikolaou
    Department/group
    • NOMATEN/Materials Structure, Informatics and Function
    Research focus

    • Materials Informatics, Multiscale Materials Modeling, Statistical Mechanics, alloy composition search and machine learning, Machine learning of strain images, machine learning of interatomic potentials

    ML expertise

    • Unsupervised Machine Learning using Principal Component Analysis and complex clustering,
      Supervised Machine Learning using deep convolutional neural networks (Tensorflow), Decision Trees, Support Vector Machines (sklearn)

    Other

    • Enjoying drinking coffee during random hours

      1. Papanikolaou, S., 2020. Microstructural inelastic fingerprints and data-rich predictions of plasticity and damage in solids. Computational Mechanics, 66(1), pp.141-154. https://link.springer.com/article/10.1007/s00466-020-01845-x
      2. Yang, Z., Papanikolaou, S., Reid, A.C., Liao, W.K., Choudhary, A.N., Campbell, C. and Agrawal, A., 2020. Learning to predict crystal plasticity at the nanoscale: Deep residual networks and size effects in uniaxial compression discrete dislocation simulations. Scientific reports, 10(1), pp.1-14. https://www.nature.com/articles/s41598-020-65157-z
      3. Papanikolaou, S. and Tzimas, M., 2019. Effects of rate, size, and prior deformation in microcrystal plasticity. Mechanics and Physics of Solids at Micro‐and Nano‐Scales, pp.25-54.
      4. Papanikolaou, S., Tzimas, M., Reid, A.C. and Langer, S.A., 2019. Spatial strain correlations, machine learning, and deformation history in crystal plasticity. Physical Review E, 99(5), p.053003. https://journals.aps.org/pre/abstract/10.1103/PhysRevE.99.053003
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  • Tomasz Krakowski
    Department/group
    • DTJ/TJ2
    Research focus

    • Galaxy evolution

    ML expertise

    • SVM, FEM

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  • William Pearson
    Department/group
    • DBP/BP4/
    Research focus

    • galaxy formation, galaxy evolution, galaxy mergers

    ML expertise

    • Artificial Neural Networks, Convolutional Neural Networks, Auto-Encoders, Generative Adversarial Networks

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List of publications
  • Direct detection of plasticity onset through total-strain profile evolution (2021), Stefanos Papanikolaou and Mikko J. Alava
  • Nanoindentation of single crystalline Mo: Atomistic defect nucleation and thermomechanical stability., F. J. Domínguez-Gutiérrez, S. Papanikolaou, A. Esfandiarpour, P. Sobkowicz, M. Alava
  • Computational study of crystal defects formation in Mo by machine learned molecular dynamics simulations, Modelling Simul. Mater. Sci. Eng. (2021), F. J. Dominguez-Gutierrez, J. Byggmaestar, K. Nordlund et al.
  • Probing the transition from dislocation jamming to pinning by machine learning, Materials Theory 4 (1), 1-16 (2020), H. Salmenjoki , L. Laurson, M.J. Alava