Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

Symbolic regression, the task of predicting the mathematical expression of a function from
the observation of its values, is a difficult task which usually involves a two-step procedure …

Many promising approaches to symbolic regression have been presented in recent years,
yet progress in the field continues to suffer from a lack of uniform, robust, and transparent …

The last few years have witnessed a surge in methods for symbolic regression, from
advances in traditional evolutionary approaches to novel deep learning-based systems …

Symbolic regression (SR) is the study of algorithms that automate the search for analytic
expressions that fit data. While recent advances in deep learning have generated renewed …

Symbolic regression is the task of identifying a mathematical expression that best fits a
provided dataset of input and output values. Due to the richness of the space of …

New constraints on the expansion rate of the Universe seem to favor evolving dark energy in
the form of thawing quintessence models, ie, models for which a canonical, minimally …

Genetic Programming (GP) is a dynamic field of research where empirical testing plays an
important role in validating new ideas and algorithms. The ability to easily prototype new …

Symbolic regression (SR) is the problem of learning a symbolic expression from numerical
data. Recently, deep neural models trained on procedurally-generated synthetic datasets …

Many real-world systems can be naturally described by mathematical formulas. The task of
automatically constructing formulas to fit observed data is called symbolic regression …