Data Science Chair

    Andrzej Dulny, M.Sc.

    Andrzej Dulny, M.Sc.

    Chair of Data Science (Informatik X)
    University of Würzburg
    Campus Hubland Nord
    Emil-Fischer-Straße 50
    97074 Würzburg

    Email: andrzej.dulny[at]

    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 ]
    • Liquor-HGNN: A heterogene...
      Schaller, M., Steininger, M., Dulny, A., Schlör, D., and Hotho, A. (2023) Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks, LWDA’23: Lernen, Wissen, Daten, Analysen. October 09--11, 2023, Marburg, Germany.
    • Wankerl, S., Dulny, A., Götz, G., and Hotho, A. (2023) Can Neural Networks Distinguish High-school Level Mathematical Concepts?, in Accepted at ICDM.
    • DynaBench: A Benchmark Da...
      Dulny, A., Hotho, A., and 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:
    • TaylorPDENet: Learning PD...
      Heinisch, P., Dulny, A., Krause, A., and Hotho, A. (2023) TaylorPDENet: Learning PDEs from non-grid Data, available: