Sorry for the rant... I just feel like abstracts are the most important part of a paper and get annoyed with poor ones. A better abstract would arguably be som

Sorry for the rant... I just feel like abstracts are the most important part of a paper and get annoyed with poor ones. A better abstract would arguably be something they have a few paragraphs into their introduction: "To construct high fidelity emulators with limited training data, the machine learning models need to have a good prior on the simulation models. Most work to date in building emulators using random forests, Gaussian Processes,or other machine learning models, do not fully capture the correlation among the output points,limiting their accuracy in emulating simulations with one, two, or three-dimensional output signals. On the other hand, convolutional neural network (CNN) have shown to have a good prior on natural signals, making them suitable for processing natural n-dimensional signals. However, as the CNN priors inherently rely on their architectures,8one has to find an architecture that gives the suitable prior of a given problem. Manually searching for the right architecture can take a significant amount of time and domain-specific expertise and often produces sub-optimal results. Here we propose to solve this problem by employing efficient neural architecture search to simultaneously find the neural network architecture that is best suited for a given case and train it. With the efficient neural architecture search and a novel super-architecture presented in this work, the algorithm can find and train fast emulators for a wide range of applications while offering major improvements in terms of accuracy compared with other techniques, even when the training data is limited."