Symbolic regression is commonly considered in wide-ranging applications due to its inherent capability for learning both structure and weighting parameters of an interpretable model. However, for ...

Symbolic regression is an advanced computational method to find mathematical equations that best explain a dataset. Unlike traditional regression, which fits data to predefined models, symbolic ...

Because of the important limitations imposed by the physical units linked with the data, generic symbolic regression algorithms frequently fail in this situation. The team has shared that Φ-SO, on the ...

About Symbolic Identification of Non-linear Dynamics. The method generalizes the SINDy algorithm by combining sparse and genetic-programming-based symbolic regression.