Performance Comparison of Design Optimization and Deep Learning-based Inverse Design

TL;DR

This paper compares traditional surrogate model-based optimization with deep learning-based inverse design, highlighting their respective advantages and limitations through benchmark problems to guide future applications.

Contribution

It provides a comprehensive performance comparison between traditional optimization and deep learning inverse design, offering practical guidelines for future use.

Findings

Deep learning inverse design offers real-time solutions without iterative optimization.

Traditional methods excel in complex, constrained scenarios.

Guidelines for selecting appropriate design methods are proposed.

Abstract

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.