  Evolutionary symbolic regression (SR) fits a symbolic equation to data, which
gives a concise interpretable model. We explore using SR as a method to propose
which data to gather in an active learning setting with physical constraints.
SR with active learning proposes which experiments to do next. Active learning
is done with query by committee, where the Pareto frontier of equations is the
committee. The physical constraints improve proposed equations in very low data
settings. These approaches reduce the data required for SR and achieves state
of the art results in data required to rediscover known equations.
