キーワード: Machine learning
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5 件のレコードが見つかりました。 1件目から5件目まで表示します。

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1-s2.0-S0169743925000085-main.pdf
MADGUI: Multi-Application Design Graphical User Interface for active learning assisted by Bayesian optimization
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI
キーワード
Graphical user interface, Statistical analysis, Machine learning, Bayesian optimization, Process optimization, User-friendly
刊行年月日
2025-01-21
更新時刻
2025-01-27 12:30:30 +0900
lin-et-al-2025-determination-of-stable-proton-configurations-by-black-box-optimization-using-an-ising-machine.pdf
Determination of Stable Proton Configurations by Black-Box Optimization Using an Ising Machine
ジャーナル論文
著者
Jianbo Lin (author) (この著者で検索)
;
Tomofumi Tada (author) (この著者で検索)
ORCID ;
Ai Koizumi (author) (この著者で検索)
;
Masato Sumita (author) (この著者で検索)
ORCID ; ORCID SAMURAI ; ORCID SAMURAI
キーワード
Machine learning, Ising machine, Proton configuration
刊行年月日
2025-02-06
更新時刻
2025-05-23 08:30:18 +0900
Manuscript.pdf
Data-driven optimization of FePt heat-assisted magnetic recording media accelerated by deep learning TEM image segmentation
ジャーナル論文
著者
N. Kulesh (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
A. Bolyachkin (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
I. Suzuki (author) (この著者で検索)
National Institute for Materials Science
ORCID ;
Y.K. Takahashi (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
H. Sepehri-Amin (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
K. Hono (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
Heat-assisted magnetic recording (HAMR), FePt, Deep learning, Machine learning, Image segmentation
刊行年月日
2023-05-27
更新時刻
2025-05-27 08:30:20 +0900
Abstracts_Krishnan.pdf
From Random Networks to AI-Driven Glass Design
プレゼンテーション
著者
N. M. Anoop Krishnan (author) (この著者で検索)
Indian Institute of Technology Delhi Department of Civil Engineering
キーワード
Machine learning, AI-Driven Glass Design
刊行年月日
更新時刻
2025-09-25 16:30:42 +0900
Fujii-DigitalDiscovery2025.pdf
A straightforward gradient-based approach for designing superconductors with high critical temperature: exploiting domain knowledge via adaptive constraints
ジャーナル論文
著者
Akihiro Fujii (author) (この著者で検索)
;
Anh Khoa Augustin Lu (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Koji Shimizu (author) (この著者で検索)
;
Satoshi Watanabe (author) (この著者で検索)
National Institute for Materials Science
ORCID
キーワード
Gradient-based optimization, Superconductor, Domain knowledge integration, Materials discovery, Critical temperature, Machine learning
刊行年月日
2025-10-29
更新時刻
2026-05-18 14:53:14 +0900
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