activity
20172025
most citedData-driven topology design using a deep generative model

59 citations · 81 across the 10 of their papers we have counts for

collaborators

10 papers

math.OC2025

Wasserstein crossover for evolutionary algorithm-based topology optimization

Taisei Kii, Kentaro Yaji, Hiroshi Teramoto +1

Evolutionary algorithms (EAs) are promising approaches for non-differentiable or strongly multimodal topology optimization problems, but they often suffer from the curse of dimensi…

math.NA2024

Adjoint lattice kinetic scheme for topology optimization in fluid problems

Yuta Tanabe, Kentaro Yaji, Kuniharu Ushijima

This paper proposes a topology optimization method for non-thermal and thermal fluid problems using the Lattice Kinetic Scheme (LKS).LKS, which is derived from the Lattice Boltzman…

cs.LG2024★ 4 cited

Deep Concept Identification for Generative Design

Ryo Tsumoto, Kentaro Yaji, Yutaka Nomaguchi +1

A generative design based on topology optimization provides diverse alternatives as entities in a computational model with a high design degree. However, as the diversity of the ge…

math.OC2024★ 1 cited

Data-driven topology design with persistent homology for enhancing population diversity

Taisei Kii, Kentaro Yaji, Hiroshi Teramoto +1

This paper proposes a selection strategy for enhancing population diversity in data-driven topology design (DDTD), a topology optimization framework based on evolutionary algorithm…

math.OC2024★ 8 cited

Data-driven multifidelity topology design with multi-channel variational auto-encoder for concurrent optimization of multiple design variable fields

Hiroki Kawabe, Kentaro Yaji, Yuichiro Aoki

The objective of this study is to establish a gradient-free topology optimization framework that facilitates more global solution searches to avoid entrapping in undesirable local…

cs.LG2024★ 7 cited

Data-driven topology design based on principal component analysis for 3D structural design problems

Jun Yang, Kentaro Yaji, Shintaro Yamasaki

Topology optimization is a structural design methodology widely utilized to address engineering challenges. However, sensitivity-based topology optimization methods struggle to sol…

physics.comp-ph2020★ 59 cited

Data-driven topology design using a deep generative model

Shintaro Yamasaki, Kentaro Yaji, Kikuo Fujita

In this paper, we propose a sensitivity-free and multi-objective structural design methodology called data-driven topology design. It is schemed to obtain high-performance material…

cs.CE2020★ 2 cited

Topology design of two-fluid heat exchange

Hiroki Kobayashi, Kentaro Yaji, Shintaro Yamasaki +1

Heat exchangers are devices that typically transfer heat between two fluids. The performance of a heat exchanger such as heat transfer rate and pressure loss strongly depends on th…

math.OC2018

Novel flow field design for vanadium redox flow batteries via topology optimization

Chih-Hsiang Chen, Kentaro Yaji, Shintaro Yamasaki +2

This paper presents a three-dimensional topology optimization method for the design of flow field in vanadium redox flow batteries (VRFBs). We focus on generating a novel flow fiel…

astro-ph.HE2017

Semi-Analytical Model for Wind-Fed Black Hole High-Mass X-ray Binaries -- State Transition Triggered by Magnetic Fields from the Companion Star --

Kentaro Yaji, Shinya Yamada, Kuniaki Masai

We propose a mechanism of state transition in wind-fed black hole binaries (high-mass X-ray binaries) such as Cyg X-1 and LMC X-1. Modeling a line-driven stellar wind from the comp…