Gabriel Kronberger

DeepAI AI Chat

Featured Co-authors

Stefan Wagner
71 publications
Michael Kommenda
19 publications
Michael Affenzeller
18 publications
Fabrício Olivetti de França
14 publications
Bogdan Burlacu
10 publications
Lukas Kammerer
6 publications
J. Ignacio Hidalgo
5 publications
Stephan Winkler
4 publications
Andreas Beham
3 publications
Christian Haider
3 publications
Stephan M. Winkler
2 publications

research

∙ 07/03/2023

Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction

People with diabetes must carefully monitor their blood glucose levels, ...

0 Daniel Parra, et al. ∙

research

∙ 12/19/2022

Steel Phase Kinetics Modeling using Symbolic Regression

We describe an approach for empirical modeling of steel phase kinetics b...

0 David Piringer, et al. ∙

research

∙ 09/28/2022

Identifying Differential Equations to predict Blood Glucose using Sparse Identification of Nonlinear Systems

Describing dynamic medical systems using machine learning is a challengi...

32 David Jödicke, et al. ∙

research

∙ 09/20/2022

Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization

Fast Function Extraction (FFX) is a deterministic algorithm for solving ...

0 Lukas Kammerer, et al. ∙

research

∙ 09/20/2022

Comparing Shape-Constrained Regression Algorithms for Data Validation

Industrial and scientific applications handle large volumes of data that...

0 Florian Bachinger, et al. ∙

research

∙ 09/14/2022

Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles

Symbolic regression is a nonlinear regression method which is commonly p...

0 Fabrício Olivetti de França, et al. ∙

research

∙ 09/02/2022

Local Optimization Often is Ill-conditioned in Genetic Programming for Symbolic Regression

Gradient-based local optimization has been shown to improve results of g...

0 Gabriel Kronberger, et al. ∙

research

∙ 06/13/2022

Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data

Particle-based modeling of materials at atomic scale plays an important ...

0 Bogdan Burlacu, et al. ∙

research

∙ 09/28/2021

Cluster Analysis of a Symbolic Regression Search Space

In this chapter we take a closer look at the distribution of symbolic re...

0 Gabriel Kronberger, et al. ∙

research

∙ 09/28/2021

Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication

Symbolic regression is a powerful system identification technique in ind...

0 Lukas Kammerer, et al. ∙

research

∙ 09/01/2021

Complexity Measures for Multi-objective Symbolic Regression

Multi-objective symbolic regression has the advantage that while the acc...

0 Michael Kommenda, et al. ∙

research

∙ 08/24/2021

Data Aggregation for Reducing Training Data in Symbolic Regression

The growing volume of data makes the use of computationally intense mach...

0 Lukas Kammerer, et al. ∙

research

∙ 08/06/2021

Smooth Symbolic Regression: Transformation of Symbolic Regression into a Real-valued Optimization Problem

The typical methods for symbolic regression produce rather abrupt change...

0 Erik Pitzer, et al. ∙

research

∙ 08/06/2021

Concept Drift Detection with Variable Interaction Networks

The current development of today's production industry towards seamless ...

0 Jan Zenisek, et al. ∙

research

∙ 08/03/2021

Extending a Physics-Based Constitutive Model using Genetic Programming

In material science, models are derived to predict emergent material pro...

0 Gabriel Kronberger, et al. ∙

research

∙ 07/29/2021

Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications

With the increasing number of created and deployed prediction models and...

0 Florian Bachinger, et al. ∙

research

∙ 07/22/2021

Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression

We introduce in this paper a runtime-efficient tree hashing algorithm fo...

0 Bogdan Burlacu, et al. ∙

research

∙ 07/20/2021

Using Shape Constraints for Improving Symbolic Regression Models

We describe and analyze algorithms for shape-constrained symbolic regres...

0 Christian Haider, et al. ∙

research

∙ 07/19/2021

Predicting Friction System Performance with Symbolic Regression and Genetic Programming with Factor Variables

Friction systems are mechanical systems wherein friction is used for for...

0 Gabriel Kronberger, et al. ∙

research

∙ 07/06/2021

Identification of Dynamical Systems using Symbolic Regression

We describe a method for the identification of models for dynamical syst...

0 Gabriel Kronberger, et al. ∙

research

∙ 03/29/2021

Shape-constrained Symbolic Regression – Improving Extrapolation with Prior Knowledge

We investigate the addition of constraints on the function image and its...

0 Gabriel Kronberger, et al. ∙

research

∙ 02/03/2019

Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure

Diversity represents an important aspect of genetic programming, being d...

0 Bogdan Burlacu, et al. ∙

research

∙ 09/23/2013

Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset

In this paper a data mining approach for variable selection and knowledg...

0 Michael Kommenda, et al. ∙

research

∙ 09/23/2013

On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection

Genetic programming is a powerful heuristic search technique that is use...

0 Gabriel Kronberger, et al. ∙

research

∙ 06/02/2013

Declarative Modeling and Bayesian Inference of Dark Matter Halos

Probabilistic programming allows specification of probabilistic models i...

0 Gabriel Kronberger, et al. ∙

research

∙ 05/16/2013

Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming

In this contribution we describe an approach to evolve composite covaria...

0 Gabriel Kronberger, et al. ∙

research

∙ 12/10/2012

Macro-Economic Time Series Modeling and Interaction Networks

Macro-economic models describe the dynamics of economic quantities. The ...

0 Gabriel Kronberger, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world