京都大学 · 工学部・工学研究科

研究キーワードmaterials informatics・machine learning screening・rheological constitutive modeling・sparse identification・high-entropy alloy simulations・multiscale polymer rheology

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• Multiscale simulations for viscoelastic fluids with approximate constitutive models derived by a sparse identification methodRheologica Acta,
• Composition-based machine learning for screening superconducting ternary hydrides from a curated datasetAPL Machine Learning,
• Machine-learned constitutive relations for multi-scale simulations of well-entangled polymer meltsPhysics of Fluids,
• Stretch-orientation-induced reduction of friction in well-entangled bidisperse blends: a dual slip-link simulation studyRheologica Acta,

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