  Deep learning (DL) is revolutionizing the scientific computing community. To
reduce the data gap, active learning has been identified as a promising
solution for DL in the scientific computing community. However, the deep active
learning (DAL) literature is dominated by image classification problems and
pool-based methods. Here we investigate the robustness of pool-based DAL
methods for scientific computing problems (dominated by regression) where DNNs
are increasingly used. We show that modern pool-based DAL methods all share an
untunable hyperparameter, termed the pool ratio, denoted $\gamma$, which is
often assumed to be known apriori in the literature. We evaluate the
performance of five state-of-the-art DAL methods on six benchmark problems if
we assume $\gamma$ is \textit{not} known - a more realistic assumption for
scientific computing problems. Our results indicate that this reduces the
performance of modern DAL methods and that they sometimes can even perform
worse than random sampling, creating significant uncertainty when used in
real-world settings. To overcome this limitation we propose, to our knowledge,
the first query synthesis DAL method for regression, termed NA-QBC. NA-QBC
removes the sensitive $\gamma$ hyperparameter and we find that, on average, it
outperforms the other DAL methods on our benchmark problems. Crucially, NA-QBC
always outperforms random sampling, providing more robust performance benefits.
