  The widely-accepted intuition that the important properties of solids are
determined by a few key variables underpins many methods in physics. Though
this reductionist paradigm is applicable in many physical problems, its utility
can be limited because the intuition for identifying the key variables often
does not exist or is difficult to develop. Machine learning algorithms (genetic
programming, neural networks, Bayesian methods, etc.) attempt to eliminate the
a priori need for such intuition but often do so with increased computational
burden and human time. A recently-developed technique in the field of signal
processing, compressive sensing (CS), provides a simple, general, and efficient
way of finding the key descriptive variables. CS is a new paradigm for model
building-we show that its models are just as robust as those built by current
state-of-the-art approaches, but can be constructed at a fraction of the
computational cost and user effort.
