30 citations · 120 across the 7 of their papers we have counts for
7 papers
Neuro-Evolutionary Approach to Physics-Aware Symbolic Regression
Jiří Kubalík, Robert Babuška
Symbolic regression is a technique that can automatically derive analytic models from data. Traditionally, symbolic regression has been implemented primarily through genetic progra…
Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression
Jiří Kubalík, Erik Derner, Robert Babuška
Many real-world systems can be described by mathematical models that are human-comprehensible, easy to analyze and help explain the system's behavior. Symbolic regression is a meth…
SymFormer: End-to-end symbolic regression using transformer-based architecture
Martin Vastl, Jonáš Kulhánek, Jiří Kubalík +2
Many real-world problems can be naturally described by mathematical formulas. The task of finding formulas from a set of observed inputs and outputs is called symbolic regression.…
An Integrated Approach to Goal Selection in Mobile Robot Exploration
Miroslav Kulich, Jiří Kubalík, Libor Přeučil
This paper deals with the problem of autonomous navigation of a mobile robot in an unknown 2D environment to fully explore the environment as efficiently as possible. We assume a t…
Symbolic Regression Driven by Training Data and Prior Knowledge
J. Kubalík, E. Derner, R. Babuška
In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do n…
Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression
Erik Derner, Jiří Kubalík, Nicola Ancona +1
Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decis…
Symbolic Regression Methods for Reinforcement Learning
Jiří Kubalík, Erik Derner, Jan Žegklitz +1
Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithm…