3 papers
cs.LG2026
Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression
Julia Reuter, Fabricio Olivetti de Franca
Symbolic regression (SR) is a class of methods that systematically explore the space of mathematical functions to discover models that accurately capture the underlying relationshi…
physics.comp-ph2025
Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches
Julia Reuter, Hani Elmestikawy, Sanaz Mostaghim +1
Drag forces on particles in random assemblies can be accurately estimated through particle-resolved direct numerical simulations (PR-DNS). Despite its limited applicability to rela…
cs.RO2025
The Road to Learning Explainable Inverse Kinematic Models: Graph Neural Networks as Inductive Bias for Symbolic Regression
Pravin Pandey, Julia Reuter, Christoph Steup +1
This paper shows how a Graph Neural Network (GNN) can be used to learn an Inverse Kinematics (IK) based on an automatically generated dataset. The generated Inverse Kinematics is g…