  We present a machine learning method to estimate the physical parameters of
classical pulsating stars such as RR Lyrae and Cepheid variables based on an
automated comparison of their theoretical and observed light curve parameters
at multiple wavelengths. We train artificial neural networks (ANNs) on
theoretical pulsation models to predict the fundamental parameters (mass,
radius, luminosity, and effective temperature) of Cepheid and RR Lyrae stars
based on their period and light curve parameters. The fundamental parameters of
these stars can be estimated up to 60 percent more accurately when the light
curve parameters are taken into consideration. This method was applied to the
observations of hundreds of Cepheids and thousands of RR Lyrae in the
Magellanic Clouds to produce catalogs of estimated masses, radii, luminosities,
and other parameters of these stars.
