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. (C) 2018 Elsevier Ltd. All rights reserved.