  In computer-aided engineering design, the goal of a designer is to find an
optimal design on a given requirement using the numerical simulator in loop
with an optimization method. In this design optimization process, a good design
optimization process is one that can reduce the time from inception to design.
In this work, we take a class of design problem, that is computationally cheap
to evaluate but has high dimensional design space. In such cases, traditional
surrogate-based optimization does not offer any benefits. In this work, we
propose an alternative way to use ML model to surrogate the design process that
formulates the search problem as an inverse problem and can save time by
finding the optimal design or at least a good initial seed design for
optimization. By using this trained surrogate model with the traditional
optimization method, we can get the best of both worlds. We call this as
Surrogate Assisted Optimization (SAO)- a hybrid approach by mixing ML surrogate
with the traditional optimization method. Empirical evaluations of propeller
design problems show that a better efficient design can be found in fewer
evaluations using SAO.

Machine Learning