74 citations · 241 across the 31 of their papers we have counts for
7 papers · 1 filter
Steel Phase Kinetics Modeling using Symbolic Regression
David Piringer, Bernhard Bloder, Gabriel Kronberger
We describe an approach for empirical modeling of steel phase kinetics based on symbolic regression and genetic programming. The algorithm takes processed data gathered from dilato…
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…
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…
Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles
Fabricio Olivetti de Franca, Gabriel Kronberger
Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming. Quantification of uncertainty of…
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…