Performance Comparison of Design Optimization and Deep Learning-based
  Inverse Design
Minyoung Jwa Jihoon Kim Seungyeon Shin Ah-hyeon Jin Dongju Shin Namwoo Kang
Surrogate model-based optimization has been increasingly used in the field of
engineering design. It involves creating a surrogate model with objective
functions or constraints based on the data obtained from simulations or
real-world experiments, and then finding the optimal solution from the model
using numerical optimization methods. Recent advancements in deep
learning-based inverse design methods have made it possible to generate
real-time optimal solutions for engineering design problems, eliminating the
requirement for iterative optimization processes. Nevertheless, no
comprehensive study has yet closely examined the specific advantages and
disadvantages of this novel approach compared to the traditional design
optimization method. The objective of this paper is to compare the performance
of traditional design optimization methods with deep learning-based inverse
design methods by employing benchmark problems across various scenarios. Based
on the findings of this study, we provide guidelines that can be taken into
account for the future utilization of deep learning-based inverse design. It is
anticipated that these guidelines will enhance the practical applicability of
this approach to real engineering design problems.