  Deep learning (DL) is revolutionizing the scientific computing community. To
reduce the data gap caused by usually expensive simulations or experimentation,
active learning has been identified as a promising solution for the scientific
computing community. However, the deep active learning (DAL) literature is
currently dominated by image classification problems and pool-based methods,
which are not directly transferrable to scientific computing problems,
dominated by regression problems with no pre-defined 'pool' of unlabeled data.
Here for the first time, we investigate the robustness of DAL methods for
scientific computing problems using ten state-of-the-art DAL methods and eight
benchmark problems. We show that, to our surprise, the majority of the DAL
methods are not robust even compared to random sampling when the ideal pool
size is unknown. We further analyze the effectiveness and robustness of DAL
methods and suggest that diversity is necessary for a robust DAL for scientific
computing problems.
