Machine Learning Network at NCBJ

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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

Relevant publications
    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

Department/group
  • Nomaten /

http://nomaten.ncbj.gov.pl/

Research focus

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

ML expertise

  • Neural Networks, Sobolev Training, Active learning.

Relevant publications

  • D. Lanzoni, F. Rovaris and F. Montalenti, “Machine learning Dislocation Interactions: Reaching the Macroscopic Scale” in preparation (2021)

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.

Relevant publications
    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

     

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

Relevant publications
    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
Department/group
  • DBP /BP4

https://www.ncbj.gov.pl/bp4/dr-hab-katarzyna-malek
https://kasiamalek.weebly.com/

Research focus

  • galaxy evolution, dust attenuation 

ML expertise

  • SVM, FEM

Relevant publications
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

Relevant publications
    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)
Department/group
  • NOMATEN /Materials Complexity

silviabonfantiphysics.com

Research focus

  • Amorphous solids, Mechanical Metamaterials, Biophysics.

ML expertise

  • CNN, SVM.

Relevant publications
    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.
Department/group
  • NOMATEN /Materials Structure, Informatics and Function

http://nomaten.ncbj.gov.pl/

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

Relevant publications
    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
Department/group
  • DTJ /TJ2
Research focus

  • Galaxy evolution

ML expertise

  • SVM, FEM

Relevant publications
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

Relevant publications


This project has received funding from the European Union Horizon 2020 research and innovation
programme under grant agreement No 857470 and from European Regional Development Fund
via Foundation for Polish Science International Research Agenda PLUS programme grant
No MAB PLUS/2018/8.
Poland
The project is co-financed from the state budget within the framework of the undertaking of the Minister of Science and Higher Education "Support for the activities of Centers of Excellence established under Horizon 2020".

Grant: 5 143 237,70 EUR
Total value: 29 971 365,00 EUR
Date of signing the funding agreement: December 2023

The purpose of the undertaking is to support entities of the higher education and science system that have received funding from the European Union budget in the competition H2020-WIDESPREAD-2018-2020/WIDESPREAD-01-2018-2019: Teaming Phase 2. in the preparation, implementation and updating of activities, maintenance of material resources necessary for carrying out activities, acquisition and modernization of scientific and research apparatus, maintenance and development of personnel potential necessary for the implementation of activities, and dissemination of the results of scientific activities.