Class Symbolic Regression: Gotta Fit 'Em All
Wassim Tenachi Rodrigo Ibata Thibaut L. François Foivos I. Diakogiannis
We introduce "Class Symbolic Regression" a first framework for automatically
finding a single analytical functional form that accurately fits multiple
datasets - each governed by its own (possibly) unique set of fitting
parameters. This hierarchical framework leverages the common constraint that
all the members of a single class of physical phenomena follow a common
governing law. Our approach extends the capabilities of our earlier Physical
Symbolic Optimization ($\Phi$-SO) framework for Symbolic Regression, which
integrates dimensional analysis constraints and deep reinforcement learning for
symbolic analytical function discovery from data. We demonstrate the efficacy
of this novel approach by applying it to a panel of synthetic toy case datasets
and showcase its practical utility for astrophysics by successfully extracting
an analytic galaxy potential from a set of simulated orbits approximating
stellar streams.