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.

翻译：进化符号回归（SR）通过拟合符号方程至数据，生成简洁可解释的模型。我们探索将SR作为在物理约束下主动学习场景中提出数据采集策略的方法。带有主动学习的SR能够建议下一步需执行的实验。本研究的主动学习采用委员会查询策略，其中帕累托前沿方程构成委员会。在极低数据条件下，物理约束能有效改进所提出的方程。这些方法减少了SR所需的数据量，并在重现已知方程所需数据量方面达到了最优结果。