Machine learning methods are increasingly deployed to construct surrogate models for complex physical systems at a reduced computational cost. However, the predictive capability of these surrogates degrades in the presence of noisy, sparse or dynamic data. We introduce an online learning method empowered by optimizer-driven sampling that has two advantages over current approaches: it ensures that all local extrema (including endpoints) of the model response surface are included in the training data, and it employs a continuous validation and update process in which surrogates undergo retraining when their performance falls below a validity threshold. We find, using benchmark functions, that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema even when the scoring metric is biased towards assessing overall accuracy. Finally, the application to dense nuclear matter demonstrates that highly accurate surrogates for a nuclear equation-of-state model can be reliably autogenerated from expensive calculations using few model evaluations.

机器学习方法越来越多地用于以降低的计算成本构建复杂物理系统的替代模型。然而，在存在噪声、稀疏或动态数据的情况下，这些替代物的预测能力会降低。我们引入了一种由优化器驱动采样支持的在线学习方法，与当前方法相比，该方法具有两个优点：它确保模型响应面的所有局部极值（包括端点）都包含在训练数据中，并且它采用连续验证和更新当代理人的表现低于有效性阈值时，代理人接受再培训的过程。我们发现，使用基准函数，优化器引导的采样在局部极值的准确性方面通常优于传统采样方法，即使评分指标偏向于评估整体准确性。最后，对致密核物质的应用表明，可以使用很少的模型评估从昂贵的计算中可靠地自动生成核状态方程模型的高精度替代物。