144 citations
- Johannes Kepler University of LinzAT14 papers
- Universidade Federal do ABCBR7 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- University of PortsmouthGB4 papers
- Institut d'Astrophysique de ParisFR3 papers
- Research Center for Non Destructive Testing (Austria)AT3 papers
- Sorbonne UniversitéFR3 papers
- University of OxfordGB3 papers
- Boston Children's HospitalUS2 papers
- Harvard UniversityUS2 papers
- Software Competence Center Hagenberg (Austria)AT2 papers
- Technische Hochschule UlmDE2 papers
8 papers · 1 filter
Identifying Differential Equations to predict Blood Glucose using Sparse Identification of Nonlinear Systems
David Jödicke, Daniel Parra, Gabriel Kronberger +1
Describing dynamic medical systems using machine learning is a challenging topic with a wide range of applications. In this work, the possibility of modeling the blood glucose leve…
Shape-constrained Symbolic Regression with NSGA-III
Christian Haider
Shape-constrained symbolic regression (SCSR) allows to include prior knowledge into data-based modeling. This inclusion allows to ensure that certain expected behavior is better re…
Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization
Lukas Kammerer, Gabriel Kronberger, Michael Kommenda
Fast Function Extraction (FFX) is a deterministic algorithm for solving symbolic regression problems. We improve the accuracy of FFX by adding parameters to the arguments of nonlin…
Comparing Shape-Constrained Regression Algorithms for Data Validation
Florian Bachinger, Gabriel Kronberger
Industrial and scientific applications handle large volumes of data that render manual validation by humans infeasible. Therefore, we require automated data validation approaches t…
Local Optimization Often is Ill-conditioned in Genetic Programming for Symbolic Regression
Gabriel Kronberger
Gradient-based local optimization has been shown to improve results of genetic programming (GP) for symbolic regression. Several state-of-the-art GP implementations use iterative n…
Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data
Bogdan Burlacu, Michael Kommenda, Gabriel Kronberger +2
Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle sim…