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In computer-aided engineering design, the goal of a designer is to find anoptimal design on a given requirement using the numerical simulator in loopwith an optimization method. In this design optimization process, a good designoptimization 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 cheapto evaluate but has high dimensional design space. In such cases, traditionalsurrogate-based optimization does not offer any benefits. In this work, wepropose an alternative way to use ML model to surrogate the design process thatformulates the search problem as an inverse problem and can save time byfinding the optimal design or at least a good initial seed design foroptimization. By using this trained surrogate model with the traditionaloptimization method, we can get the best of both worlds. We call this asSurrogate Assisted Optimization (SAO)- a hybrid approach by mixing ML surrogatewith the traditional optimization method. Empirical evaluations of propellerdesign problems show that a better efficient design can be found in fewerevaluations using SAO.

Fusion of ML with numerical simulation for optimized propeller design