  Optimizing fluid-dynamic performance is an important engineering task.
Traditionally, experts design shapes based on empirical estimations and verify
them through expensive experiments. This costly process, both in terms of time
and space, may only explore a limited number of shapes and lead to sub-optimal
designs. In this research, a test-proven deep learning architecture is applied
to predict the performance under various restrictions and search for better
shapes by optimizing the learned prediction function. The major challenge is
the vast amount of data points Deep Neural Network (DNN) demands, which is
improvident to simulate. To remedy this drawback, a Frequentist active learning
is used to explore regions of the output space that DNN predicts promising.
This operation reduces the number of data samples demanded from ~8000 to 625.
The final stage, a user interface, made the model capable of optimizing with
given user input of minimum area and viscosity. Flood fill is used to define a
boundary area function so that the optimal shape does not bypass the minimum
area. Stochastic Gradient Langevin Dynamics (SGLD) is employed to make sure the
ultimate shape is optimized while circumventing the required area. Jointly,
shapes with extremely low drags are found explored by a practical user
interface with no human domain knowledge and modest computation overhead.
