Systematization of data-driven optimum design incorporating a deep generative model

2022 Fiscal Year Final Research Report

• Project/Area Number: 20KK0329
• Research Category: Fund for the Promotion of Joint International Research (Fostering Joint International Research (A))
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 18030:Design engineering-related
• Research Institution: Osaka University
• Principal Investigator: Yaji Kentaro 大阪大学, 大学院工学研究科, 助教 (90779373)
• Project Period (FY): 2020 – 2022
• Keywords: トポロジー最適化 / 深層学習 / 深層生成モデル

Outline of Final Research Achievements

In the fundamental research project (Research Project: 20H02054), we are aiming to develop a new framework for optimal designs using topology optimization for flow batteries, which have gained attention as next-generation energy storage systems. Furthermore, this framework has the potential to be applied to design problems that are difficult to solve directly, not limited to flow batteries, and can be systematized as a general-purpose framework. The key lies in the introduction of deep generative models into this framework and the establishment of a solid mathematical foundation. Therefore, we conducted an international collaborative research project that spans the fields of design engineering and data science, in collaboration with researchers from the University of Texas at Austin, specializing in the field of data science. This collaboration aims to achieve a significant advancement in the fundamental research project.

Free Research Field

最適設計

Academic Significance and Societal Importance of the Research Achievements

一部の研究者によってトポロジー最適化に深層学習を組み込むことで最適構造を推定する取り組みが報告されているものの、いずれの先行研究も計算時間の短縮に主眼が置かれており、「深層学習を用いたからこそ解ける」という例は未だ数少ないのが現状である。また、深層学習は瞬時に最適構造を推定する可能性を秘めているものの、学習にある程度の時間を要するため、一概に高速化を実現できるわけではない。このような背景を踏まえ、「従来のトポロジー最適化では解くことができない問題を解く」ことを目的とし、汎用的なデータ駆動型最適設計法の構築を目指すところに本研究の学術的新規性があることを強調しておきたい。