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/
    Research focus

    • Numerical modeling of nanoindentation of single crystal materials and high entropy metal alloys. Radiation in materials at extreme.

    ML expertise

    • Supervised learning with linear regression, Gaussian Approximation Potential Framework.

      1.  F. J. Dominguez-Gutierrez, J. Byggmaestar, K. Nordlund et al. “On the classification and quantification of crystal defects after energetic bombardment by machine learning molecular dynamics simulations”, Nucl. Mater. and Energy 22, 100724 (2020)
      2. F. J. Dominguez-Gutierrez, J. Byggmaestar, K. Nordlund et al. “Computational study of crystal defects formation in Mo by machine learned molecular dynamics simulations''. Modelling Simul. Mater. Sci. Eng. (2021) . https://doi.org/10.1088/1361-651X/abf152

       

       

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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