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Ergebnis der Suche nach: "Machine Learning"
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26441 - 26450 von 29091
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Artikel 26441 Probing Collectivity in String Models via Machine Learning
Enthalten in Physics of particles and nuclei letters Bd. 22, 23.4.2025, Nr. 1, date:2.2025: 90-94
Online Ressource
Artikel 26442 Probing dark QCD sector through the Higgs portal with machine learning at the LHC
Enthalten in Journal of high energy physics Bd. 2023, 28.8.2023, Nr. 8, date:8.2023: 1-44
Online Ressource
Artikel 26443 Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach
Enthalten in Nature Communications Bd. 16, 16.4.2025, Nr. 1, date:12.2025: 1-13
Online Ressource
Artikel 26444 Probing out-of-distribution generalization in machine learning for materials
Enthalten in Communications materials Bd. 6, 11.1.2025, Nr. 1, date:12.2025: 1-10
Online Ressource
Artikel 26445 Probing the Rheological Properties of Liquids Under Conditions of Elastohydrodynamic Lubrication Using Simulations and Machine Learning
Enthalten in Tribology letters Bd. 69, 27.5.2021, Nr. 3, date:9.2021: 1-19
Online Ressource
Artikel 26446 Probing the transition from dislocation jamming to pinning by machine learning
Enthalten in Materials theory Bd. 4, 9.10.2020, Nr. 1, date:12.2020: 1-16
Online Ressource
Artikel 26447 Probing Vegetation, Climatic Data, and Machine Learning for Agricultural Planning and Climate Action: A Case Study from North India
Enthalten in Remote sensing in earth systems sciences Bd. 8, 30.12.2024, Nr. 1, date:3.2025: 213-231
Online Ressource
Artikel 26448 Procedure Model for the Use of Machine Learning
Enthalten in ATZ worldwide Bd. 126, 23.2.2024, Nr. 2-3, date:2.2024: 48-52
Online Ressource
Artikel 26449 Process Design of Laser Powder Bed Fusion of Stainless Steel Using a Gaussian Process-Based Machine Learning Model
Enthalten in JOM Bd. 72, 23.9.2019, Nr. 1, date:1.2020: 420-428
Online Ressource
Artikel 26450 Process–Material–Performance Trade-off Exploration of Materials Sintering with Machine Learning Models
Enthalten in Integrating materials and manufacturing innovation Bd. 13, 13.11.2024, Nr. 4, date:12.2024: 927-941
Online Ressource


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