When building physical models (truncated expansions, force-fields,
etc.), one often employs the widely-accepted intuition that the
physics is determined by a few dominant terms. But this reductionist
paradigm is limited because the intuition for identifying the those
terms 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. Compressive sensing (a new technique in the field of
signal processing) provides a simple, general, and efficient solution
to this challenge. CS-generated models are just as robust as
those built by current state-of-the-art approaches, but can be
constructed at a very small fraction of the computational cost and
user effort. CS provides a revolutionary advance
for some instances of model building.
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