Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …

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 …

Modeling complex physical dynamics is a fundamental task in science and engineering.
Traditional physics-based models are sample efficient, and interpretable but often rely on …

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 …

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

Symbolic regression (SR), exploring mathematical expressions from a given data set to
construct an interpretable model, emerges as a powerful computational technique with the …

Equation discovery, also known as symbolic regression, is a machine learning task of
inducing closed-form equations from data and background knowledge. The latter takes …

Mathematical equations have been unreasonably effective in describing complex natural
phenomena across various scientific disciplines. However, discovering such insightful …

The paper surveys automated scientific discovery, from equation discovery and symbolic
regression to autonomous discovery systems and agents. It discusses the individual …

Mechanistic dynamical models allow us to study the behavior of complex biological systems.
They can provide an objective and quantitative understanding that would be difficult to …