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 …

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 task of learning a model of data in the form of a
mathematical expression. By their nature, SR models have the potential to be accurate and …

Symbolic regression (SR) is a challenging task in machine learning that involves finding a
mathematical expression for a function based on its values. Recent advancements in SR …

We develop a transformer-based sequence-to-sequence model that recovers scalar
ordinary differential equations (ODEs) in symbolic form from irregularly sampled and noisy …

We introduce ODEFormer, the first transformer able to infer multidimensional ordinary
differential equation (ODE) systems in symbolic form from the observation of a single …

Symbolic regression (SR) can be utilized to unveil the underlying mathematical expressions
that describe a given set of observed data. At present, SR can be categorized into two …

In symbolic regression, the objective is to find an analytical expression that accurately fits
experimental data with the minimal use of mathematical symbols such as operators …

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 …

Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural
networks to meta-learn configurations of traditional black-box optimizers. Despite their …