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

Discovering a meaningful symbolic expression that explains experimental data is a
fundamental challenge in many scientific fields. We present a novel, open-source …

We describe and analyze algorithms for shape-constrained symbolic regression, which
allow the inclusion of prior knowledge about the shape of the regression function. This is …

Most engineering domains abound with models derived from first principles that have
beenproven to be effective for decades. These models are not only a valuable source of …

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 …

An interpretable machine learning method, physics-informed genetic programming-based
symbolic regression (P-GPSR), is integrated into a continuum thermodynamic approach to …

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 …

In some situations, the interpretability of the machine learning models plays a role as
important as the model accuracy. Interpretability comes from the need to trust the prediction …

Shape-constrained Symbolic Regression integrates prior knowledge about the function
shape into the symbolic regression model. This can be used to enforce that the model has …