38 citations · 62 across the 9 of their papers we have counts for
9 papers
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…
Quantum Extreme Reservoir Computing for Phase Classification of Polymer Alloy Microstructures
Arisa Ikeda, Akitada Sakurai, Kae Nemoto +1
Quantum machine learning (QML) is expected to offer new opportunities to process high-dimensional data efficiently by exploiting the exponentially large state space of quantum syst…
Hamiltonian simulation for nonlinear partial differential equation by Schrödingerization
Shoya Sasaki, Katsuhiro Endo, Mayu Muramatsu
Hamiltonian simulation is a fundamental algorithm in quantum computing that has attracted considerable interest owing to its potential to efficiently solve the governing equations…
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…
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…
Implementation of spectral methods on Ising machines: toward flow simulations on quantum annealer
Kenichiro Takagi, Naoki Moriya, Shiori Aoki +3
We investigate the possibility and current limitations of flow computations using quantum annealers by solving a fundamental flow problem on Ising machines. As a fundamental proble…
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…
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…
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
Yusuke Yamazaki, Ali Harandi, Mayu Muramatsu +5
We propose a novel finite element-based physics-informed operator learning framework that allows for predicting spatiotemporal dynamics governed by partial differential equations (…