Towards Improving Simulations of Flows around Spherical Particles Using Genetic Programming. - Julia Reuter; Manoj Cendrollu; Fabien Evrard; Sanaz Mostaghim; Berend G. M. van Wachem (CEC, 2022)

Genetic Programming-Based Inverse Kinematics for Robotic Manipulators. - Julia Reuter; Christoph Steup; Sanaz Mostaghim (EuroGP, 2022)

Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows. - Julia Reuter; Hani Elmestikawy; Fabien Evrard; Sanaz Mostaghim; Berend G. M. van Wachem (EuroGP, 2023)

Multi-Objective Island Model Genetic Programming for Predicting the Stokes Flow around a Sphere. - Julia Reuter; Pravin Pandey; Sanaz Mostaghim (SSCI, 2023)

Unit-Aware Genetic Programming for the Development of Empirical Equations. - Julia Reuter; Viktor Martinek; Roland Herzog; Sanaz Mostaghim (PPSN (1), 2024)

Shape Constraints in Symbolic Regression using Penalized Least Squares. - Viktor Martinek; Julia Reuter; Ophelia Frotscher; Sanaz Mostaghim; Markus Richter; Roland Herzog (CoRR, 2024)

Data for the paper: "Deterministic drag modelling for spherical particles in Stokes regime using data-driven approaches". - Hani Elmestikawy; Julia Reuter; Fabien Evrard; Sanaz Mostaghim; Berend G. M. van Wachem (Zenodo, 2024)

The Road to Learning Explainable Inverse Kinematic Models: Graph Neural Networks as Inductive Bias for Symbolic Regression. - Pravin Pandey; Julia Reuter; Christoph Steup; Sanaz Mostaghim (CoRR, 2025)