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

• URL: http://arxiv.org/abs/2308.13000v1
• Date: Wed, 23 Aug 2023 06:04:54 GMT
• Title: Performance Comparison of Design Optimization and Deep Learning-based Inverse Design
• Authors: Minyoung Jwa, Jihoon Kim, Seungyeon Shin, Ah-hyeon Jin, Dongju Shin, Namwoo Kang
• Abstract summary: This paper compares the performance of traditional design optimization methods with deep learning-based inverse design methods. It provides guidelines that can be taken into account for the future utilization of deep learning-based inverse design.
• Score: 15.620304857903069
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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