5 papers
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…
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…
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…
Shape Constraints in Symbolic Regression using Penalized Least Squares
Viktor Martinek, Julia Reuter, Ophelia Frotscher +3
We study the addition of shape constraints (SC) and their consideration during the parameter identification step of symbolic regression (SR). SC serve as a means to introduce prior…
Unit-Aware Genetic Programming for the Development of Empirical Equations
Julia Reuter, Viktor Martinek, Roland Herzog +1
When developing empirical equations, domain experts require these to be accurate and adhere to physical laws. Often, constants with unknown units need to be discovered alongside th…