  Designing an inexpensive approximate surrogate model that captures the
salient features of an expensive high-fidelity behavior is a prevalent approach
in design optimization. In recent times, Deep Learning (DL) models are being
used as a promising surrogate computational model for engineering problems.
However, the main challenge in creating a DL-based surrogate is to
simulate/label a large number of design points, which is time-consuming for
computationally costly and/or high-dimensional engineering problems. In the
present work, we propose a novel sampling technique by combining the active
learning (AL) method with DL. We call this method $\epsilon$-weighted hybrid
query strategy ($\epsilon$-HQS) , which focuses on the evaluation of the
surrogate at each learning iteration and provides an estimate of the failure
probability of the surrogate in the Design Space. By reusing already collected
training and test data, the learned failure probability guides the next
iteration's sampling process to the region of the high probability of failure.
During the empirical evaluation, better accuracy of the surrogate was observed
in comparison to other methods of sample selection. We empirically evaluated
this method in two different engineering design domains, finite element based
static stress analysis of submarine pressure vessel(computationally costly
process) and second submarine propeller design( high dimensional problem).
https://github.com/vardhah/epsilon_weighted_Hybrid_Query_Strategy
