Data Science Chair

    Andrzej Dulny, M.Sc.

    Andrzej Dulny, M.Sc.

    Chair of Data Science (Informatik X)
    University of Würzburg
    Am Hubland
    97074 Würzburg

    Email: andrzej.dulny@uni-wuerzburg.de

    Phone: (+49 931)  31 - 81316

    Fax: (+49 931) 31 81316-0

    Projects and Research Interests

    I have been part of the DMIR Research Group since early 2021, after I received my master's degree in Mathematics at the University of Würzburg. Currently pursuing research at Prof. Hotho's research group, I am passionate about leveraging machine learning techniques to predict the evolution of physical systems, extract the physical equations governing the system, and enhance simulation methods with machine learning. My work lies at the intersection of machine learning and physics, with a particular emphasis on developing and applying novel deep learning methods to solve complex problems in the field of dynamical systems using:

    • Physics-Informed Neural Networks (PINNs)
    • NeuralODEs
    • Graph Neural Networks
    • Transformer-based models for spatial data

    Furthermore, I am particularily interested in handling non-grid and low-resolution data.

    The methods I develop in my research can be applied to improve simulation and prediction of a variety of physical systems including weather prediction, climate models, wave simulations, fluid dynamics etc. Additionally, I apply the results of my research within the MAGNET4cardiac7T research project, where I aim to train neural networks for simulating electromagnetic fields within a MRI scanner.


    • 2017–2020: M.Sc. Mathematics at the University of Würzburg
    • 2013–2016: B.Sc. Mathematics at the Jagiellonian University in Cracow


    2023[ to top ]
    • Heinisch, P., Dulny, A., Krause, A., Hotho, A. (2023) TaylorPDENet: Learning PDEs from non-grid Data, available: http://arxiv.org/abs/2306.14511.
    • Dulny, A., Hotho, A., Krause, A. (2023) DynaBench: A Benchmark Dataset for Learning Dynamical Systems from Low-Resolution Data, in Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E. and Bonchi, F., eds., Machine Learning and Knowledge Discovery in Databases: Research Track, Cham: Springer Nature Switzerland, 438–455, available: http://arxiv.org/abs/2306.05805.
    2022[ to top ]
    • Dulny, A., Hotho, A., Krause, A. (2022) NeuralPDE: Modelling Dynamical Systems from Data, in Bergmann, R., Malburg, L., Rodermund, S.C. and Timm, I.J., eds., KI 2022: Advances in Artificial Intelligence, Cham: Springer International Publishing, 75–89.
    2021[ to top ]
    • Wankerl, S., Dulny, A., Götz, G., Hotho, A. (2021) Learning Mathematical Relations Using Deep Tree Models, in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 1681–1687, available: https://doi.org/10.1109/ICMLA52953.2021.00268.
    • Kobs, K., Steininger, M., Dulny, A., Hotho, A. (2021) Do Different Deep Metric Learning Losses Lead to Similar Learned Features?, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 10644–10654.
    2020[ to top ]
    • Dulny, A., Steininger, M., Lautenschlager, F., Krause, A., Hotho, A. (2020) Evaluating the multi-task learning approach for land use regression modelling of air pollution, in International Conference on Frontiers of Artificial Intelligence and Machine Learning, IASED.