PySR is an open-source library for practical symbolic regression, a type of machine learning
which aims to discover human-interpretable symbolic models. PySR was developed to …

The finite element method can be used to compute accurate stress intensity factors (SIFs) for
cracks with complex geometries and boundary conditions. In contrast, handbook solutions …

Operon is a C++ framework for symbolic regression with the ability to perform local search
by optimizing model coefficients using the Levenberg-Marquardt algorithm. This …

We propose symbolic regression as a powerful tool for the numerical studies of proposed
models of physics beyond the Standard Model. In this paper we demonstrate the efficacy of …

The rapid expansion of wind power worldwide underscores the critical significance of
engineering-focused analytical wake models in both the design and operation of wind farms …

In today's competitive and dynamic global markets, rapidly designing processes for
formulated products–complex blends such as cosmetics, detergents, or personal care goods …

This paper tackles the challenge of symbolic regression (SR) with a vast mathematical
expression space, where the primary difficulty lies in accurately identifying subspaces that …

Genetic programming-based evolutionary feature construction is a widely used technique for
automatically enhancing the performance of a regression algorithm. While it has achieved …

With the development of high performance computer and experimental technology, the study
of turbulence has accumulated a large number of high fidelity data. However, few general …

This paper presents an interactive symbolic regression framework i-gplearn, which extends
the popular Python Symbolic Regression library gplearn with user interactivity …