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20242026
most citedA finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains

38 citations · 62 across the 9 of their papers we have counts for

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cs.CE2026

Adaptive Encoding Strategy for Quantum Annealing in Mixed-Variable Engineering Optimization

Fabian Key, Lukas Freinberger, Mayu Muramatsu +1

Mixed discrete-continuous optimization is central to engineering design, where discrete choices interact with continuous fields. These problems are difficult due to high-dimensiona…

cs.CE2024★ 8 cited

Dislocation-based crystal plasticity simulation on grain-size dependence of mechanical properties in dual-phase steels

Misato Suzuki, Mayu Muramatsu, Kazuyuki Shizawa

In this study, the effect of ferrite grain size on the mechanical properties and dislocation behavior of dual-phase (DP) steel is investigated using dislocation-based crystal plast…

cs.CE2024

An Ising Machine Formulation for Design Updates in Topology Optimization of Flow Channels

Yudai Suzuki, Shiori Aoki, Fabian Key +5

Topology optimization is an essential tool in computational engineering, for example, to improve the design and efficiency of flow channels. At the same time, Ising machines, inclu…

cs.CE2024★ 3 cited

Conditional diffusion model for inverse prediction of process parameters and dendritic microstructures from mechanical properties

Arisa Ikeda, Ryo Higuchi, Tomohiro Yokozeki +4

In this study, we develop a conditional diffusion model that proposes the optimal process parameters and predicts the microstructure for the desired mechanical properties. In mater…

cs.CE2024★ 7 cited

Investigation on optimal microstructure of dual-phase steel with high strength and ductility by machine learning

Misato Suzuki, Kazuyuki Shizawa, Mayu Muramatsu

In this study, we developed an inverse analysis framework that proposes a microstructure for dual-phase (DP) steel that exhibits high strength and ductility. The inverse analysis m…