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）通过拟合符号方程到数据，生成简洁且可解释的模型。我们探索将SR作为一种在具有物理约束的主动学习环境中提出数据收集方法的手段。结合主动学习的SR能够建议下一步应进行的实验。主动学习采用委员会查询（query by committee）的方式，其中Pareto前沿上的方程构成委员会。物理约束在极低数据量条件下改进了所提出的方程。这些方法减少了SR所需的数据量，并在所需数据量以重新发现已知方程的任务中达到了最先进水平。