We propose a learning-based paradigm (LEAPS2) to recommend the best surrogate/ with minimal computational effort using the input-output data of a complex physico-numerical system. Emulating the knowledge pyramid, LEAPS2 uses several attributes to extract system information from the data, correlates them with surrogate performances, stores this attribute-surrogate knowledge in a regression tree ensemble, and uses the ensemble to recommend surrogates for unknown systems. We implement LEAPS2 using data from 66 diverse analytical functions, 18 attributes, and 25 surrogates. By progressively adding data, we demonstrate that LEAPS2 learns to improve computational efficiency and functional accuracy. Besides, the architecture of LEAPS2 enables its evolution via more attributes and surrogates. We employ LEAPS2 to recommend surrogates for estimating the bubble and dew point temperatures of LNG. Interestingly, our assistive tool suggests a different surrogate for each temperature, and hints that DPT may be harder to approximate than BPT.

我们提出了一种基于学习的范式（LEAPS2），以推荐使用复杂物理数字系统的输入输出数据以最少的计算量进行的最佳替代/。LEAPS2模拟知识金字塔，使用几个属性从数据中提取系统信息，将它们与代理性能相关联，将此属性代理知识存储在回归树集合中，并使用该集合为未知系统推荐代理。我们使用来自66个不同分析功能，18个属性和25个替代项的数据来实现LEAPS2。通过逐步添加数据，我们证明了LEAPS2可以学习提高计算效率和功能准确性。此外，LEAPS2的体系结构还可以通过更多属性和替代来实现其演进。我们使用LEAPS2来推荐替代指标，以估算LNG的气泡和露点温度。有趣的是，我们的辅助工具针对每个温度建议了不同的替代指标，并暗示DPT可能比BPT更难估算。