  The advent of space-based observatories such as CoRoT and Kepler has enabled
the testing of our understanding of stellar evolution on thousands of stars.
Evolutionary models typically require five input parameters, the mass, initial
Helium abundance, initial metallicity, mixing- length (assumed to be constant
over time), and the age to which the star must be evolved. Some of these
parameters are also very useful in characterizing the associated planets and in
studying galactic archaeology. How to obtain these parameters from observations
rapidly and accurately, specifically in the context of surveys of thousands of
stars, is an outstanding ques- tion, one that has eluded straightforward
resolution. For a given star, we typically measure the effective temperature
and surface metallicity spectroscopically and low-degree oscillation
frequencies through space observatories. Here we demonstrate that statistical
learning, using artificial neural networks, is successful in determining the
evolutionary parameters based on spectroscopic and seismic measurements. Our
trained networks show robustness over a broad range of parameter space, and
critically, are entirely computationally inexpensive and fully automated. We
analyze the observations of a few stars using this method and the results com-
pare well to inferences obtained using other techniques. This method is both
computationally cheap and inferentially accurate, paving the way for analyzing
the vast quantities of stellar observations from past, current, and future
missions.
