  In a computer-aided engineering design optimization problem that involves
notoriously complex and time-consuming simulator, the prevalent approach is to
replace these simulations with a data-driven surrogate that approximates the
simulator's behavior at a much cheaper cost. The main challenge in creating an
inexpensive data-driven surrogate is the generation of a sheer number of data
using these computationally expensive numerical simulations. In such cases,
Active Learning (AL) methods have been used that attempt to learn an
input--output behavior while labeling the fewest samples possible. The current
trend in AL for a regression problem is dominated by the Bayesian framework
that needs training an ensemble of learning models that makes surrogate
training computationally tedious if the underlying learning model is Deep
Neural Networks (DNNs). However, DNNs have an excellent capability to learn
highly nonlinear and complex relationships even for a very high dimensional
problem. To leverage the excellent learning capability of deep networks along
with avoiding the computational complexity of the Bayesian paradigm, in this
work we propose a simple and scalable approach for active learning that works
in a student-teacher manner to train a surrogate model. By using this proposed
approach, we are able to achieve the same level of surrogate accuracy as the
other baselines like DBAL and Monte Carlo sampling with up to 40 % fewer
samples. We empirically evaluated this method on multiple use cases including
three different engineering design domains:finite element analysis,
computational fluid dynamics, and propeller design.
