Julia Reuter recently completed her PhD at the Chair of Computational Intelligence of the Otto-von-Guericke-University Magdeburg (OvGU). Julia's research on Genetic Programming for Symbolic Regression contributed to the DFG-funded project titled "Improving simulations of large-scale dense particle-laden flows with machine learning: a genetic programming approach". In May 2025, Julia successfully defensed her PhD Thesis (summa cum laude) with the title Development of Symbolic Models Using Genetic Programming and Domain Knowledge.

Before joining the CI group, Julia obtained her Bachelor's degree in mechanical engineering at the DHBW Stuttgart in 2017, in cooperation with Robert Bosch GmbH. In 2021, she finished the Digital Engineering Master's program at the Otto-von-Guericke-University in Magdeburg.

List of Publications:

• Julia Reuter, Hani Elmestikawy, Sanaz Mostaghim, Berend van Wachem
•  Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches
• Accepted for publication and presentation at 1st International Symposium on AI and Fluid Mechanics, Chania, Greece, 2025.

• Julia Reuter, Viktor Martinek, Roland Herzog, and Sanaz Mostaghim
• Unit-Aware Genetic Programming for the Development of Empirical Equations
• In: Parallel Problem Solving from Nature – PPSN XVIII. ed. by Affenzeller, M., Winkler, S. M., Kononova, A. V., Trautmann, H., Tušar, T., Machado, P., and Bäck, T. Vol. 15148. Cham: Springer Nature Switzerland, 2024, pp. 168–183. https://doi.org/10.1007/978-3-031-70055-2_11

• Viktor Martinek, Julia Reuter, Ophelia Frotscher, Sanaz Mostaghim,  Markus Richter, and Roland Herzog
• Shape Constraints in Symbolic Regression using Penalized Least Squares.
• Presented at European Conference on Machine Learning, Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), Vilnius, Lithuania, 2024. https://doi.org/10.48550/arXiv.2405.20800

• Hani Elmestikawy, Julia Reuter, Fabien Evrard, Sanaz Mostaghim, and Berend van Wachem 
• Deterministic drag modelling for spherical particles in Stokes regime using data-driven approaches 
• In: International Journal of Multiphase Flow 178 (Aug. 2024), p. 104880.  https://doi.org/10.1016/j.ijmultiphaseflow.2024.104880

• Julia Reuter, Pravin Pandey, and Sanaz Mostaghim
• Multi-Objective Island Model Genetic Programming for Predicting the Stokes Flow Around a Sphere
• 2023 IEEE Symposium Series on Computational Intelligence (SSCI), Mexico City, Mexico, 2023, pp. 1485-1490. doi: 10.1109/SSCI52147.2023.10371955

• Julia Reuter, Hani Elmestikawy, Fabien Evrard, Sanaz Mostaghim, and Berend van Wachem
• Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows.
  

• In: Pappa, G., Giacobini, M., Vasicek, Z. (eds) Genetic Programming. EuroGP 2023. Lecture Notes in Computer Science, vol 13986. Springer, Cham. https://doi.org/10.1007/978-3-031-29573-7_3

• This paper was granted the Best Paper Award at EuroGP 2023
• This paper, together with the one below (CEC 2022), received the Bronze Award at the HUMIES competition held at GECCO'23 in Lisbon, Portugal, for the entry titled Towards Improving Simulations of Flows around Spherical Particles Using Genetic Programming 

• Julia Reuter, Manoj Cendrollu, Fabien Evrard, Sanaz Mostaghim, and Berend van Wachem
• Towards Improving Simulations of Flows around Spherical Particles Using Genetic Programming.
• In: 2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, 2022, pp. 1-8, https://doi.org/10.1109/CEC55065.2022.9870301

• Julia Reuter, Christoph Steup, and Sanaz Mostaghim
• Genetic Programming-Based Inverse Kinematics for Robotic Manipulators.
• In: Medvet, E., Pappa, G., Xue, B. (eds) Genetic Programming. EuroGP 2022. Lecture Notes in Computer Science, vol 13223. https://doi.org/10.1007/978-3-031-02056-8_9

• Heiner Zille, Fabien Evrard, Julia Reuter, Sanaz Mostaghim, and Berend van Wachem
• ASSESSMENT OF MULTI-OBJECTIVE AND COEVOLUTIONARY GENETIC PROGRAMMING FOR PREDICTING THE STOKES FLOW AROUND A SPHERE
• In: Conference Proceedings EUROGEN. 2021. https://doi.org/10.7712/140121.7959.18341

For our latest research, please see the entry in our research blog here.